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Huynh H, Le K, Vu L, Nguyen T, Holcomb M, Forli S, Phan H. Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms. J Comput Chem 2024; 45:1552-1561. [PMID: 38500409 PMCID: PMC11099847 DOI: 10.1002/jcc.27329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/26/2024] [Accepted: 01/31/2024] [Indexed: 03/20/2024]
Abstract
Investigation of Lewis acid-base interactions has been conducted by ab initio calculations and machine learning (ML) models. This study aims to resolve two critical tasks that have not been quantitatively investigated. First, ML models developed from density functional theory (DFT) calculations predict experimental BF3 affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol-1. The ML models are trained by DFT-calculated BF3 affinity of more than 3000 adducts, with input features readily obtained by rdkit. Second, the ML models have the capability of predicting the relative strength of Lewis base binding atoms in Lewis polybases, which is either an extremely challenging task to conduct experimentally or a computationally expensive task for ab initio methods. The study demonstrates and solidifies the potential of combining DFT calculations and ML models to predict experimental properties, especially those that are scarce and impractical to empirically acquire.
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Affiliation(s)
- Hieu Huynh
- Fulbright University Vietnam, Ho Chi Minh city, Vietnam, Ho Chi Minh City 700000
| | - Khanh Le
- Fulbright University Vietnam, Ho Chi Minh city, Vietnam, Ho Chi Minh City 700000
| | - Linh Vu
- Fulbright University Vietnam, Ho Chi Minh city, Vietnam, Ho Chi Minh City 700000
| | - Trang Nguyen
- Fulbright University Vietnam, Ho Chi Minh city, Vietnam, Ho Chi Minh City 700000
| | - Matthew Holcomb
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037 USA
| | - Hung Phan
- Fulbright University Vietnam, Ho Chi Minh city, Vietnam, Ho Chi Minh City 700000
- Soka University of America, Aliso Viejo, California, United States, CA 92656
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2
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Draper MR, Waterman A, Dannatt JE, Patel P. Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge. Phys Chem Chem Phys 2024; 26:7907-7919. [PMID: 38376855 PMCID: PMC10938873 DOI: 10.1039/d3cp04140a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
The partition coefficient (log P) is an important physicochemical property that provides information regarding a molecule's pharmacokinetics, toxicity, and bioavailability. Methods to accurately predict the partition coefficient have the potential to accelerate drug design. In an effort to test current methods and explore new computational techniques, the statistical assessment of the modeling of proteins and ligands (SAMPL) has established a blind prediction challenge. The ninth iteration challenge was to predict the toluene-water partition coefficient (log Ptol/w) of sixteen drug molecules. Herein, three approaches are reported broadly under the categories of quantum mechanics (QM), molecular mechanics (MM), and data-driven machine learning (ML). The three blind submissions yield mean unsigned errors (MUE) ranging from 1.53-2.93 log Ptol/w units. The MUEs were reduced to 1.00 log Ptol/w for the QM methods. While MM and ML methods outperformed DFT approaches for challenge molecules with fewer rotational degrees of freedom, they suffered for the larger molecules in this dataset. Overall, DFT functionals paired with a triple-ζ basis set were the simplest and most effective tool to obtain quantitatively accurate partition coefficients.
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Affiliation(s)
- Michael R Draper
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| | - Asa Waterman
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| | | | - Prajay Patel
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
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3
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Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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4
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Martins da Silva AY, Arouche TDS, Siqueira MRS, Ramalho TC, de Faria LJG, Gester RDM, Carvalho Junior RND, Santana de Oliveira M, Neto AMDJC. SARS-CoV-2 external structures interacting with nanospheres using docking and molecular dynamics. J Biomol Struct Dyn 2023:1-16. [PMID: 37712854 DOI: 10.1080/07391102.2023.2252930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 08/22/2023] [Indexed: 09/16/2023]
Abstract
Coronavirus is caused by the SARS-CoV-2 virus has shown rapid proliferation and scarcity of treatments with proven effectiveness. In this way, we simulated the hospitalization of carbon nanospheres, with external active sites of the SARS-CoV-2 virus (M-Pro, S-Gly and E-Pro), which can be adsorbed or inactivated when interacting with the nanospheres. The computational procedures performed in this work were developed with the SwissDock server for molecular docking and the GROMACS software for molecular dynamics, making it possible to extract relevant data on affinity energy, distance between molecules, free Gibbs energy and mean square deviation of atomic positions, surface area accessible to solvents. Molecular docking indicates that all ligands have an affinity for the receptor's active sites. The nanospheres interact favorably with all proteins, showing promising results, especially C60, which presented the best affinity energy and RMSD values for all protein macromolecules investigated. The C60 with E-Pro exhibited the highest affinity energy of -9.361 kcal/mol, demonstrating stability in both molecular docking and molecular dynamics simulations. Our RMSD calculations indicated that the nanospheres remained predominantly stable, fluctuating within a range of 2 to 3 Å. Additionally, the analysis of other structures yielded promising results that hold potential for application in other proteases.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Anderson Yuri Martins da Silva
- Laboratory for the Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, Belem, Brazil
- Graduated in Chemical Engineering, ITEC, Federal University of Pará, Belém, Brazil
- Postgraduate Program in Chemical Engineering, ITEC, Federal University of Pará, Belém, Brazil
| | - Tiago da Silva Arouche
- Laboratory for the Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, Belem, Brazil
- Graduated in Chemical Engineering, ITEC, Federal University of Pará, Belém, Brazil
| | | | - Teodorico Castro Ramalho
- Postgraduate Program in Engineering of Natural Resources of the Amazon, ITEC, Federal University of Pará, Belém, Brazil
| | | | - Rodrigo do Monte Gester
- Institute of Exact Sciences (ICE), Federal University of the South and Southeast of Pará, Maraba, Brazil
| | - Raul Nunes de Carvalho Junior
- Postgraduate Program in Chemical Engineering, ITEC, Federal University of Pará, Belém, Brazil
- Postgraduate Program in Engineering of Natural Resources of the Amazon, ITEC, Federal University of Pará, Belém, Brazil
- Faculty of Food Engineering ITEC, Federal University of Pará, Belém, Brazil
| | | | - Antonio Maia de Jesus Chaves Neto
- Laboratory for the Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, Belem, Brazil
- Graduated in Chemical Engineering, ITEC, Federal University of Pará, Belém, Brazil
- Postgraduate Program in Chemical Engineering, ITEC, Federal University of Pará, Belém, Brazil
- National Professional Master's in Physics Teaching, Federal University of Pará, Belém, Brazil
- Museu Paraense Emílio Goeldi, Diretoria, Coordenação de Botânica, Rua Augusto Corrêa, Belém, Brazil
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Seckler H, Szwabiński J, Metzler R. Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories. J Phys Chem Lett 2023; 14:7910-7923. [PMID: 37646323 DOI: 10.1021/acs.jpclett.3c01351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Single-particle traces of the diffusive motion of molecules, cells, or animals are by now routinely measured, similar to stochastic records of stock prices or weather data. Deciphering the stochastic mechanism behind the recorded dynamics is vital in understanding the observed systems. Typically, the task is to decipher the exact type of diffusion and/or to determine the system parameters. The tools used in this endeavor are currently being revolutionized by modern machine-learning techniques. In this Perspective we provide an overview of recently introduced methods in machine-learning for diffusive time series, most notably, those successfully competing in the anomalous diffusion challenge. As such methods are often criticized for their lack of interpretability, we focus on means to include uncertainty estimates and feature-based approaches, both improving interpretability and providing concrete insight into the learning process of the machine. We expand the discussion by examining predictions on different out-of-distribution data. We also comment on expected future developments.
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Affiliation(s)
- Henrik Seckler
- Institute of Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
| | - Janusz Szwabiński
- Hugo Steinhaus Center, Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
| | - Ralf Metzler
- Institute of Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
- Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea
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Elkashlan M, Ahmad RM, Hajar M, Al Jasmi F, Corchado JM, Nasarudin NA, Mohamad MS. A review of SARS-CoV-2 drug repurposing: databases and machine learning models. Front Pharmacol 2023; 14:1182465. [PMID: 37601065 PMCID: PMC10436567 DOI: 10.3389/fphar.2023.1182465] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/06/2023] [Indexed: 08/22/2023] Open
Abstract
The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.
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Affiliation(s)
- Marim Elkashlan
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Malak Hajar
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Fatma Al Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Juan Manuel Corchado
- Departamento de Informática y Automática, Facultad de Ciencias, Grupo de Investigación BISITE, Instituto de Investigación Biomédica de Salamanca, University of Salamanca, Salamanca, Spain
| | - Nurul Athirah Nasarudin
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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Giron CC, Laaksonen A, Barroso da Silva FL. Differences between Omicron SARS-CoV-2 RBD and other variants in their ability to interact with cell receptors and monoclonal antibodies. J Biomol Struct Dyn 2023; 41:5707-5727. [PMID: 35815535 DOI: 10.1080/07391102.2022.2095305] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 06/23/2022] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2 remains a health threat with the continuous emergence of new variants. This work aims to expand the knowledge about the SARS-CoV-2 receptor-binding domain (RBD) interactions with cell receptors and monoclonal antibodies (mAbs). By using constant-pH Monte Carlo simulations, the free energy of interactions between the RBD from different variants and several partners (Angiotensin-Converting Enzyme-2 (ACE2) polymorphisms and various mAbs) were predicted. Computed RBD-ACE2-binding affinities were higher for two ACE2 polymorphisms (rs142984500 and rs4646116) typically found in Europeans which indicates a genetic susceptibility. This is amplified for Omicron (BA.1) and its sublineages BA.2 and BA.3. The antibody landscape was computationally investigated with the largest set of mAbs so far in the literature. From the 32 studied binders, groups of mAbs were identified from weak to strong binding affinities (e.g. S2K146). These mAbs with strong binding capacity and especially their combination are amenable to experimentation and clinical trials because of their high predicted binding affinities and possible neutralization potential for current known virus mutations and a universal coronavirus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Carolina Corrêa Giron
- Departamento de Ciências Biomoleculares, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil
- Universidade Federal do Triângulo Mineiro, Hospital de Clínicas, Uberaba, MG, Brazil
| | - Aatto Laaksonen
- Department of Materials and Environmental Chemistry, Arrhenius Laboratory, Stockholm University, Stockholm, Sweden
- State Key Laboratory of Materials-Oriented and Chemical Engineering, Nanjing Tech University, Nanjing, PR China
- Centre of Advanced Research in Bionanoconjugates and Biopolymers, Petru Poni Institute of Macromolecular Chemistry, Iasi, Romania
- Department of Engineering Sciences and Mathematics, Division of Energy Science, Luleå University of Technology, Luleå, Sweden
- Department of Chemical and Geological Sciences, University of Cagliari, Monserrato, Italy
| | - Fernando Luís Barroso da Silva
- Departamento de Ciências Biomoleculares, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA
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Dubey AK, Chabert GL, Carriero A, Pasche A, Danna PSC, Agarwal S, Mohanty L, Sharma N, Yadav S, Jain A, Kumar A, Kalra MK, Sobel DW, Laird JR, Singh IM, Singh N, Tsoulfas G, Fouda MM, Alizad A, Kitas GD, Khanna NN, Viskovic K, Kukuljan M, Al-Maini M, El-Baz A, Saba L, Suri JS. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework. Diagnostics (Basel) 2023; 13:diagnostics13111954. [PMID: 37296806 DOI: 10.3390/diagnostics13111954] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/22/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND AND MOTIVATION Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.
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Affiliation(s)
- Arun Kumar Dubey
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Alessandro Carriero
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Alessio Pasche
- Department of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Pietro S C Danna
- Department of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
| | - Lopamudra Mohanty
- ABES Engineering College, Ghaziabad 201009, India
- Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
| | - Neeraj Sharma
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
| | - Sarita Yadav
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Achin Jain
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Ashish Kumar
- Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - David W Sobel
- Men's Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Azra Alizad
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - Melita Kukuljan
- Department of Interventional and Diagnostic Radiology, Clinical Hospital Center Rijeka, 51000 Rijeka, Croatia
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology & Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Ayman El-Baz
- Biomedical Engineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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Haleem S, Albar NH, Al Fahad MS, AlWasem HO. Knowledge, Awareness, and Perception of COVID-19 and Artificial Intelligence: A Cross-Sectional Study Among the Population in Saudi Arabia. Cureus 2023; 15:e40921. [PMID: 37496534 PMCID: PMC10368304 DOI: 10.7759/cureus.40921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 06/19/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has made significant contributions to the development of medicines and vaccines. In addition, AI can analyze large amounts of COVID-19 test data, including the number of positive cases, to forecast the trajectory of the pandemic. AIM This study aimed to assess the knowledge, perception, and awareness of the general population in Saudi Arabia regarding AI and its application in combating COVID-19. METHODS A cross-sectional research design was employed, and online surveys were distributed via email and social media platforms. Purposeful sampling was used to select participants who met the inclusion criteria. The reliability and validity of the survey instrument were also assessed. RESULTS The majority of respondents (34.6%) fell within the age range of 30 to 39 years. The sample predominantly consisted of female participants. Approximately 59% of respondents reported using at least one AI tool or application on a daily basis. Furthermore, the majority of respondents agreed that digital medical services, mentioned in a previous question, could be beneficial in reducing unnecessary interactions between patients and healthcare providers. CONCLUSION The COVID-19 pandemic has demonstrated the transformative potential of AI in pandemic response. AI has played a crucial role in various aspects of combating COVID-19, including patient diagnosis, treatment development, and vaccine creation. However, challenges and limitations exist in terms of data accessibility, bias, and privacy when utilizing AI. These issues need to be addressed to ensure the ethical and responsible use of AI in the fight against COVID-19 and future pandemics.
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Affiliation(s)
- Shaista Haleem
- Aesthetic and Restorative Dentistry, Riyadh Elm University, Riyadh, SAU
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10
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Ghosh N, Saha I, Gambin A. Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19. ACS OMEGA 2023; 8:13840-13854. [PMID: 37163139 PMCID: PMC10084923 DOI: 10.1021/acsomega.3c00030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/22/2023] [Indexed: 05/11/2023]
Abstract
COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is very important to understand human-spike protein-protein interactions (PPIs) in order to predict new PPIs and consequently propose drugs for the human proteins in order to fight the virus and its different mutated variants, with the mutations occurring in the spike protein. This fact motivated us to develop a complete pipeline where PPIs and drug-protein interactions can be predicted for human-SARS-CoV-2 interactions. In this regard, initially interacting data sets are collected from the literature, and noninteracting data sets are subsequently created for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for drug-protein interactions both interacting and noninteracting data sets are considered from DrugBank and ChEMBL databases. Thereafter, a model based on a sequence-based feature is used to code the protein sequences of human and spike proteins using the well-known Moran autocorrelation technique, while the drugs are coded using another well-known technique, viz., PaDEL descriptors, to predict new human-spike PPIs and eventually new drug-protein interactions for the top 20 predicted human proteins interacting with the original spike protein and its different mutated variants like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random forest as it is found to perform better than other predictors, providing an accuracy of 90.53% for human-spike PPI and 96.15% for drug-protein interactions. Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide, dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins like ACACA, DST, DYNC1H1, etc.
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Affiliation(s)
- Nimisha Ghosh
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 00-927 Warsaw, Poland
- Department of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha 'O' Anusandhan, Bhubaneswar, 751030 Odisha, India
| | - Indrajit Saha
- Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata, 700106 West Bengal, India
| | - Anna Gambin
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 00-927 Warsaw, Poland
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Kathavarayan A, Ramasamy V, Rajamanickam R, Subramaniyan G. Synthesis, Crystal Structure, Hirshfeld Surface and Docking Studies of 2‐(methacryloyloxy)ethyl‐6‐amino‐5‐cyano‐2‐methyl‐4‐(thiophen‐2‐yl)‐4
H
‐pyran‐3‐carboxylate. ChemistrySelect 2022. [DOI: 10.1002/slct.202203680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Arulvani Kathavarayan
- Department of Chemistry PGP College of Arts and Science (Affiliated to Periyar University-Salem) Namakkal 637 207 Tamil Nadu India
| | - Venkateswaramoorthi Ramasamy
- Department of Chemistry PGP College of Arts and Science (Affiliated to Periyar University-Salem) Namakkal 637 207 Tamil Nadu India
| | - Ramachandran Rajamanickam
- Department of Chemistry PGP College of Arts and Science (Affiliated to Periyar University-Salem) Namakkal 637 207 Tamil Nadu India
| | - Gunavathi Subramaniyan
- Department of Chemistry PGP College of Arts and Science (Affiliated to Periyar University-Salem) Namakkal 637 207 Tamil Nadu India
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12
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Batra R, Loeffler TD, Chan H, Srinivasan S, Cui H, Korendovych IV, Nanda V, Palmer LC, Solomon LA, Fry HC, Sankaranarayanan SKRS. Machine learning overcomes human bias in the discovery of self-assembling peptides. Nat Chem 2022; 14:1427-1435. [PMID: 36316409 PMCID: PMC9844539 DOI: 10.1038/s41557-022-01055-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/01/2022] [Indexed: 12/23/2022]
Abstract
Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow-AI-expert-that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.
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Affiliation(s)
- Rohit Batra
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
- Department of Metallurgical and Materials Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Troy D Loeffler
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, IL, USA
| | - Henry Chan
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, IL, USA
| | - Srilok Srinivasan
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
| | - Honggang Cui
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Vikas Nanda
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ, USA
| | - Liam C Palmer
- Department of Chemistry, Northwestern University, Evanston, IL, USA
| | - Lee A Solomon
- Department of Chemistry and Biochemistry, George Mason University, Manassas, VA, USA
| | - H Christopher Fry
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA.
| | - Subramanian K R S Sankaranarayanan
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA.
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, IL, USA.
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13
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Hammoud B, Semaan A, Elhajj I, Benova L. Can machine learning models predict maternal and newborn healthcare providers' perception of safety during the COVID-19 pandemic? A cross-sectional study of a global online survey. HUMAN RESOURCES FOR HEALTH 2022; 20:63. [PMID: 35986293 PMCID: PMC9389509 DOI: 10.1186/s12960-022-00758-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Maternal and newborn healthcare providers are essential professional groups vulnerable to physical and psychological risks associated with the COVID-19 pandemic. This study uses machine learning algorithms to create a predictive tool for maternal and newborn healthcare providers' perception of being safe in the workplace globally during the pandemic. METHODS We used data collected between 24 March and 5 July 2020 through a global online survey of maternal and newborn healthcare providers. The questionnaire was available in 12 languages. To predict healthcare providers' perception of safety in the workplace, we used features collected in the questionnaire, in addition to publicly available national economic and COVID-19-related factors. We built, trained and tested five machine learning models: Support Vector Machine (SVM), Random Forest (RF), XGBoost, CatBoost and Artificial Neural Network (ANN) for classification and regression. We extracted from RF models the relative contribution of features in output prediction. RESULTS Models included data from 941 maternal and newborn healthcare providers from 89 countries. ML models performed well in classification and regression tasks, whereby RF had 82% cross-validated accuracy for classification, and CatBoost with 0.46 cross-validated root mean square error for regression. In both classification and regression, the most important features contributing to output prediction were classified as three themes: (1) information accessibility, clarity and quality; (2) availability of support and means of protection; and (3) COVID-19 epidemiology. CONCLUSION This study identified salient features contributing to maternal and newborn healthcare providers perception of safety in the workplace. The developed tool can be used by health systems globally to allow real-time learning from data collected during a health system shock. By responding in real-time to the needs of healthcare providers, health systems could prevent potential negative consequences on the quality of care offered to women and newborns.
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Affiliation(s)
- Bassel Hammoud
- Biomedical Engineering Program, Faculty of Medicine-Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
| | - Aline Semaan
- Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
| | - Imad Elhajj
- Electrical and Computer Engineering Department, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
| | - Lenka Benova
- Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
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14
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Liu XH, Cheng T, Liu BY, Chi J, Shu T, Wang T. Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development. Front Pharmacol 2022; 13:955648. [PMID: 36016554 PMCID: PMC9395726 DOI: 10.3389/fphar.2022.955648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/13/2022] [Indexed: 12/14/2022] Open
Abstract
COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.
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15
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Synthesis, crystal structure, DFT and molecular docking studies of N-acetyl-2,4-[diaryl-3-azabicyclo[3.3.1]nonan-9-yl]-9-spiro-4'-acetyl-2'-(acetylamino)-4',9-dihydro-[1',3',4']-thiadiazoles: A potential SARS-nCoV-2 Mpro (COVID-19) inhibitor. J Mol Struct 2022; 1259:132747. [PMID: 35250091 PMCID: PMC8888462 DOI: 10.1016/j.molstruc.2022.132747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/15/2022] [Accepted: 03/01/2022] [Indexed: 11/21/2022]
Abstract
In this paper, we describe the synthesis and crystal structure analysis of N-acetyl-2,4-[diphenyl-3-azabicyclo[3.3.1]nonan-9-yl]-9-spiro-4′-acetyl-2′-(acetylamino)-4′,9-dihydro-[1′,3′,4′]-thiadiazole (3a) and N-acetyl- 2,4-[bis(p-methoxyphenyl)-3-azabicyclo[3.3.1]nonan-9-yl]-9-spiro-4′-acetyl-2′-(acetylamino)-4′,9-dihydro-[1′,3′,4′]-thiadiazole (3b). The title compounds 3a and 3b are characterized by 1D NMR and single crystal x-ray diffraction analysis. Non-covalent interactions in a molecule were identified by Hirshfeld surface (dnorm contacts and 2D fingerprint plot) analysis. In addition, the existence of chalcogen bond (S•••O bond) in the molecular structures (3a and 3b) are described by NCI-RDG and QTAIM analysis. NBO analysis is employed to describe the orbital interactions and electron transfer between sulfur and oxygen atoms. Molecular docking is carried out for compounds 3a and 3b with COVID-19 viral protein SARS-nCoV-2 Mpro (PDB ID: 6LU7).
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16
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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17
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Galetsi P, Katsaliaki K, Kumar S. The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19. Soc Sci Med 2022; 301:114973. [PMID: 35452893 PMCID: PMC9001170 DOI: 10.1016/j.socscimed.2022.114973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 02/21/2022] [Accepted: 04/08/2022] [Indexed: 12/23/2022]
Abstract
With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.
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Affiliation(s)
- Panagiota Galetsi
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece
| | - Korina Katsaliaki
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece
| | - Sameer Kumar
- Opus College of Business, University of St. Thomas Minneapolis Campus 1000 LaSalle Ave, Schulze Hall 333, Minneapolis, MN, 55403, USA.
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18
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Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking. J Mol Liq 2022; 353. [PMID: 35273421 PMCID: PMC8903148 DOI: 10.1016/j.molliq.2022.118759] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.
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19
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Ghosh D, Chakraborty S, Kodamana H, Chakraborty S. Application of machine learning in understanding plant virus pathogenesis: trends and perspectives on emergence, diagnosis, host-virus interplay and management. Virol J 2022; 19:42. [PMID: 35264189 PMCID: PMC8905280 DOI: 10.1186/s12985-022-01767-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 02/27/2022] [Indexed: 12/24/2022] Open
Abstract
Background Inclusion of high throughput technologies in the field of biology has generated massive amounts of data in the recent years. Now, transforming these huge volumes of data into knowledge is the primary challenge in computational biology. The traditional methods of data analysis have failed to carry out the task. Hence, researchers are turning to machine learning based approaches for the analysis of high-dimensional big data. In machine learning, once a model is trained with a training dataset, it can be applied on a testing dataset which is independent. In current times, deep learning algorithms further promote the application of machine learning in several field of biology including plant virology. Main body Plant viruses have emerged as one of the principal global threats to food security due to their devastating impact on crops and vegetables. The emergence of new viral strains and species help viruses to evade the concurrent preventive methods. According to a survey conducted in 2014, plant viruses are anticipated to cause a global yield loss of more than thirty billion USD per year. In order to design effective, durable and broad-spectrum management protocols, it is very important to understand the mechanistic details of viral pathogenesis. The application of machine learning enables precise diagnosis of plant viral diseases at an early stage. Furthermore, the development of several machine learning-guided bioinformatics platforms has primed plant virologists to understand the host-virus interplay better. In addition, machine learning has tremendous potential in deciphering the pattern of plant virus evolution and emergence as well as in developing viable control options. Conclusions Considering a significant progress in the application of machine learning in understanding plant virology, this review highlights an introductory note on machine learning and comprehensively discusses the trends and prospects of machine learning in the diagnosis of viral diseases, understanding host-virus interplay and emergence of plant viruses.
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Affiliation(s)
- Dibyendu Ghosh
- Molecular Virology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Srija Chakraborty
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Hariprasad Kodamana
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India.,School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Supriya Chakraborty
- Molecular Virology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.
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20
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Zahoránszky-Kőhalmi G, Siramshetty VB, Kumar P, Gurumurthy M, Grillo B, Mathew B, Metaxatos D, Backus M, Mierzwa T, Simon R, Grishagin I, Brovold L, Mathé EA, Hall MD, Michael SG, Godfrey AG, Mestres J, Jensen LJ, Oprea TI. A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. J Chem Inf Model 2022; 62:718-729. [PMID: 35057621 PMCID: PMC10790216 DOI: 10.1021/acs.jcim.1c00431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.
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Affiliation(s)
| | - Vishal B. Siramshetty
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Praveen Kumar
- Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, Albuquerque, NM 87131, USA
- Department of Computer Science, University of New Mexico, 1 University of New Mexico Albuquerque, NM 87131, USA
| | - Manideep Gurumurthy
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Busola Grillo
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Biju Mathew
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Dimitrios Metaxatos
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Mark Backus
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Tim Mierzwa
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Reid Simon
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Ivan Grishagin
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
- Rancho BioSciences LLC., 16955 Via Del Campo Suite 200, San Diego, CA 92127, USA
| | - Laura Brovold
- Rancho BioSciences LLC., 16955 Via Del Campo Suite 200, San Diego, CA 92127, USA
| | - Ewy A. Mathé
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Matthew D. Hall
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Samuel G. Michael
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Alexander G. Godfrey
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Jordi Mestres
- Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Lars J. Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences,University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
| | - Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, Albuquerque, NM 87131, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences,University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
- UNM Comprehensive Cancer Center, 1201 Camino de Salud NE, Albuquerque, NM 87102, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
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21
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Singh SP, Bhatnagar A, Singh SK, K Patra S, Kanwar N, Kanwal A, Amar S, Manna R. SARS-CoV-2 Infections, Impaired Tissue, and Metabolic Health: Pathophysiology and Potential Therapeutics. Mini Rev Med Chem 2022; 22:2102-2123. [PMID: 35105287 DOI: 10.2174/1389557522666220201154845] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/09/2021] [Accepted: 12/21/2021] [Indexed: 01/08/2023]
Abstract
The SARS-CoV-2 enters the human airways and comes into contact with the mucous membranes lining the mouth, nose, and eyes. The virus enters the healthy cells and uses cell machinery to make several copies of the virus. Critically ill patients infected with SARS-CoV-2 may have damaged lungs, air sacs, lining, and walls. Since COVID-19 causes cytokine storm, it damages the alveolar cells of the lungs and fills them with fluid, making it harder to exchange oxygen and carbon dioxide. The SARS-CoV-2 infection causes a range of complications, including mild to critical breathing difficulties. It has been observed that older people suffering from health conditions like cardiomyopathies, nephropathies, metabolic syndrome, and diabetes instigate severe symptoms. Many people who died due to COVID-19 had impaired metabolic health [IMH], characterized by hypertension, dyslipidemia, and hyperglycemia, i.e., diabetes, cardiovascular system, and renal diseases making their retrieval challenging. Jeopardy stresses for increased mortality from COVID-19 include older age, COPD, ischemic heart disease, diabetes mellitus, and immunosuppression. However, no targeted therapies are available as of now. Almost two-thirds of diagnosed coronavirus patients had cardiovascular diseases and diabetes, out of which 37% were under 60. The NHS audit revealed that with a higher expression of ACE-2 receptors, viral particles could easily bind their protein spikes and get inside the cells, finally causing COVID-19 infection. Hence, people with IMH are more prone to COVID-19 and, ultimately, comorbidities. This review provides enormous information about tissue [lungs, heart and kidneys] damage, pathophysiological changes, and impaired metabolic health of SARS-CoV-2 infected patients. Moreover, it also designates the possible therapeutic targets of COVID-19 and drugs which can be used against these targets.
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Affiliation(s)
| | - Aayushi Bhatnagar
- Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan, India-305817
| | - Sujeet Kumar Singh
- Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan, India-305817
| | - Sanjib K Patra
- Department of Yoga, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan, India-305817
| | - Navjot Kanwar
- Department of Pharmacology, All India Institute of Medical Sciences, Bathinda, Punjab, India-151001
| | - Abhinav Kanwal
- Department of Pharmacology, All India Institute of Medical Sciences, Bathinda, Punjab, India-151001
| | - Salomon Amar
- Department of Pharmacology, New York Medical College, Valhalla, NY 10595
| | - Ranata Manna
- Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan, India-305817
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22
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Bucinsky L, Bortňák D, Gall M, Matúška J, Milata V, Pitoňák M, Štekláč M, Végh D, Zajaček D. Machine learning prediction of 3CLpro SARS-CoV-2 docking scores. Comput Biol Chem 2022; 98:107656. [PMID: 35288359 PMCID: PMC8881816 DOI: 10.1016/j.compbiolchem.2022.107656] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/14/2022]
Abstract
Molecular docking results of two training sets containing 866 and 8,696 compounds were used to train three different machine learning (ML) approaches. Neural network approaches according to Keras and TensorFlow libraries and the gradient boosted decision trees approach of XGBoost were used with DScribe’s Smooth Overlap of Atomic Positions molecular descriptors. In addition, neural networks using the SchNetPack library and descriptors were used. The ML performance was tested on three different sets, including compounds for future organic synthesis. The final evaluation of the ML predicted docking scores was based on the ZINC in vivo set, from which 1,200 compounds were randomly selected with respect to their size. The results obtained showed a consistent ML prediction capability of docking scores, and even though compounds with more than 60 atoms were found slightly overestimated they remain valid for a subsequent evaluation of their drug repurposing suitability.
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23
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Structure-based inhibitor design and repurposing clinical drugs to target SARS-CoV-2 proteases. Biochem Soc Trans 2022; 50:151-165. [PMID: 35015073 DOI: 10.1042/bst20211180] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 01/01/2023]
Abstract
SARS-CoV-2, the coronavirus responsible for the current COVID-19 pandemic, encodes two proteases, 3CLpro and PLpro, two of the main antiviral research targets. Here we provide an overview of the structures and functions of 3CLpro and PLpro and examine strategies of structure-based drug designing and drug repurposing against these proteases. Rational structure-based drug design enables the generation of potent and target-specific antivirals. Drug repurposing offers an attractive prospect with an accelerated turnaround. Thus far, several protease inhibitors have been identified, and some candidates are undergoing trials that may well prove to be effective antivirals against SARS-CoV-2.
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24
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Novak J, Potemkin VA. A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study. Mol Divers 2022; 26:2631-2645. [PMID: 35001230 PMCID: PMC8743077 DOI: 10.1007/s11030-021-10355-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/21/2021] [Indexed: 11/03/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Its main protease, 3C-like protease (3CLpro), is an attractive target for drug design, due to its importance in virus replication. The analysis of the radial distribution function of 159 3CLpro structures reveals a high similarity index. A study of the catalytic pocket of 3CLpro with bound inhibitors reveals that the influence of the inhibitors is local, perturbing dominantly only residues in the active pocket. A machine learning based model with high predictive ability against SARS-CoV-2 3CLpro is designed and validated. The model is used to perform a drug-repurposing study, with the main aim to identify existing drugs with the highest 3CLpro inhibition power. Among antiviral agents, lopinavir, idoxuridine, paritaprevir, and favipiravir showed the highest inhibition potential. Enzyme - ligand interactions as a key ingredient for successful drug design.
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Affiliation(s)
- Jurica Novak
- Higher Medical and Biological School, Laboratory of Computational Modeling of Drugs, South Ural State University, Tchaikovsky Str. 20-A, Chelyabinsk, 454080, Russia.
| | - Vladimir A Potemkin
- Higher Medical and Biological School, Laboratory of Computational Modeling of Drugs, South Ural State University, Tchaikovsky Str. 20-A, Chelyabinsk, 454080, Russia
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25
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Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model. Metab Eng 2022; 69:286-301. [PMID: 34982997 DOI: 10.1016/j.ymben.2021.12.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/16/2021] [Accepted: 12/29/2021] [Indexed: 11/20/2022]
Abstract
Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via 13C-metabolic flux analysis (13C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated 13C labeling data were used with 13C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.
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26
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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27
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Prates ET, Garvin MR, Jones P, Miller JI, Sullivan KA, Cliff A, Gazolla JGFM, Shah MB, Walker AM, Lane M, Rentsch CT, Justice A, Pavicic M, Romero J, Jacobson D. Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach. Methods Mol Biol 2022; 2452:317-351. [PMID: 35554915 DOI: 10.1007/978-1-0716-2111-0_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.
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Affiliation(s)
- Erica T Prates
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Michael R Garvin
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Piet Jones
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - J Izaak Miller
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Kyle A Sullivan
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Ashley Cliff
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Joao Gabriel Felipe Machado Gazolla
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Manesh B Shah
- Genome Science and Technology, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Angelica M Walker
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Matthew Lane
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- VA Connecticut Healthcare/General Internal Medicine, West Haven, CT, USA
| | - Amy Justice
- VA Connecticut Healthcare/General Internal Medicine, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Mirko Pavicic
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA
| | - Jonathon Romero
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Daniel Jacobson
- Oak Ridge National Laboratory, Computational Systems Biology, Oak Ridge, TN, USA.
- National Virtual Biotechnology Laboratory, US Department of Energy, Washington, DC, USA.
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA.
- Genome Science and Technology, University of Tennessee Knoxville, Knoxville, TN, USA.
- Department of Psychology, NeuroNet Research Center, University of Tennessee Knoxville, Knoxville, TN, USA.
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28
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Aronskyy I, Masoudi-Sobhanzadeh Y, Cappuccio A, Zaslavsky E. Advances in the computational landscape for repurposed drugs against COVID-19. Drug Discov Today 2021; 26:2800-2815. [PMID: 34339864 PMCID: PMC8323501 DOI: 10.1016/j.drudis.2021.07.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/30/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023]
Abstract
The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
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Affiliation(s)
- Illya Aronskyy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Corresponding authors
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Corresponding authors
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29
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Masoudi-Sobhanzadeh Y, Salemi A, Pourseif MM, Jafari B, Omidi Y, Masoudi-Nejad A. Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries. Brief Bioinform 2021; 22:bbab113. [PMID: 33993214 PMCID: PMC8194848 DOI: 10.1093/bib/bbab113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 02/15/2021] [Accepted: 03/13/2021] [Indexed: 01/09/2023] Open
Abstract
To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.
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Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Aysan Salemi
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammad M Pourseif
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behzad Jafari
- Department of Medicinal Chemistry, Faculty of Pharmacy, Urmia University of Medical Sciences, Urmia, Iran
| | - Yadollah Omidi
- Nova Southeastern University College of Pharmacy, Florida, USA
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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30
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Dey J, Sarkar A, Karforma S. ICT-Guided Glycemic Information Sharing Through Artificial Neural Telecare Network. SN COMPUTER SCIENCE 2021; 2:426. [PMID: 34458859 PMCID: PMC8381865 DOI: 10.1007/s42979-021-00818-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 08/12/2021] [Indexed: 11/27/2022]
Abstract
The revolutionary and retrospective changes in the use of ICT have propelled the introduction of telecare health services in the crucial corona virus pandemic times. There have been revolutionary changes that happened with the advent of this novel corona virus. The proposed technique is based on secured glycemic information sharing between the server and users using artificial neural computational learning suite. Using symmetric Tree Parity Machines (TPMs) at the server and user ends, salp swarm-based session key has been generated for the proposed glycemic information modular encryption. The added taste of this paper is that without exchanging the entire session key, both TPMs will get full synchronized in terms of their weight vectors. With rise in the intake of highly rated Glycemic Indexed (GI) foods in today’s COVID-19 lockdown lifestyle, it contributes a lot in the formation of cavities inside the periodontium, and several other diseases likes of COPD, Type I and Type II DM. GI-based food pyramid depicts the merit of the food in the top to bottom spread up approach. High GI food items helps in more co-morbid diseases in patients. It is recommended to have foods from the lower radars of the food pyramid. The proposed encryption with salp swarm-generated key has been more resistant to Man-In-The-Middle attacks. Different mathematical tests were carried on this proposed technique. The outcomes of those tests have proved its efficacy, an acceptance of the proposed technique. The total cryptographic time observed on four GI modules was 0.956 ms, 0.468 ms, 0.643 ms, and 0.771 ms.
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Affiliation(s)
- Joydeep Dey
- Department of Computer Science, M.U.C Women’s College, Burdwan, India
| | - Arindam Sarkar
- Department of Computer Science and Electronics, Ramakrishna Mission Vidyamandira, Belur, India
| | - Sunil Karforma
- Department of Computer Science, The University of Burdwan, Burdwan, India
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31
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Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne) 2021; 8:704256. [PMID: 34660623 PMCID: PMC8514781 DOI: 10.3389/fmed.2021.704256] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 08/09/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.
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Affiliation(s)
- Lian Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Yonggang Zhang
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dongguang Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Xiang Tong
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Tao Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Shijie Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Jizhen Huang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Li Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Lingmin Chen
- Department of Anesthesiology and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University and The Research Units of West China, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hong Fan
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Mike Clarke
- Northern Ireland Methodology Hub, Queen's University Belfast, Belfast, United Kingdom
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32
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Kumar A, Kolnure SN, Abhishek K, Fadi-Al-Turjman, Nerurkar P, Ghalib MR, Shankar A. Advanced deep learning algorithms for infectious disease modeling using clinical data- A Case Study on CoVID-19. Curr Med Imaging 2021; 18:570-582. [PMID: 34503419 DOI: 10.2174/1573405617666210908125911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/28/2021] [Accepted: 07/02/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Infectious disease happens when an individual is defiled by a micro-organism/virus from another person or an animal. It is troublesome that causes hurt at both individual and huge scope scales. CASE PRESENTATION The ongoing episode of COVID-19 ailment brought about by the new coronavirus first distinguished in Wuhan China, and its quick spread far and wide, revived the consideration of the world towards the impacts of such plagues on individual's regular daily existence. We attempt to exploit this effectiveness of Advanced deep learning algorithms to predict the Growth of Infectious disease based on time series data and classification based on (symptoms) text data and X-ray image data. CONCLUSION Goal is identifying the nature of the phenomenon represented by the sequence of observations and forecasting.
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Affiliation(s)
- Ajay Kumar
- Dept. of Computer Science & Engineering, NIT Patna, Bihar. India
| | | | - Kumar Abhishek
- Dept. of Computer Science & Engineering, NIT Patna, Bihar. India
| | - Fadi-Al-Turjman
- Research Centre for AI and IoT, Department of Artificial Intelligence Engineering, Near East University, Nicosia, Mersin 10. Turkey
| | - Pranav Nerurkar
- Dept. of CE & IT, VJTI Dept. of Data Science, MPSTME, NMIMS University, Mumbai. India
| | | | - Achyut Shankar
- Department of CSE, Amity School of Engineering and Technology, Amity University, Uttar Pradesh. India
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33
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Selvaraj C, Dinesh DC, Krafcikova P, Boura E, Aarthy M, Pravin MA, Singh SK. Structural Understanding of SARS-CoV-2 Drug Targets, Active Site Contour Map Analysis and COVID-19 Therapeutics. Curr Mol Pharmacol 2021; 15:418-433. [PMID: 34488601 DOI: 10.2174/1874467214666210906125959] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 03/11/2021] [Accepted: 03/17/2021] [Indexed: 11/22/2022]
Abstract
The most iconic word of the year 2020 is 'COVID-19', the shortened name for coronavirus disease 2019. The pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is responsible for multiple worldwide lockdowns, an economic crisis, and a substantial increase in hospitalizations for viral pneumonia along with respiratory failure and multiorgan dysfunctions. Recently, the first few vaccines were approved by World Health Organization (WHO) and can eventually save millions of lives. Even though, few emergency use drugs like Remdesivir and several other repurposed drugs, still there is no approved drug for COVID-19. The coronaviral encoded proteins involved in host-cell entry, replication, and host-cell invading mechanism are potentially therapeutic targets. This perspective review provides the molecular overview of SARS-CoV-2 life cycle for summarizing potential drug targets, structural insights, active site contour map analyses of those selected SARS-CoV-2 protein targets for drug discovery, immunology, and pathogenesis.
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Affiliation(s)
- Chandrabose Selvaraj
- Computer Aided Drug Design and Molecular Modeling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi-630004, Tamil Nadu. India
| | | | - Petra Krafcikova
- Institute of Organic Chemistry and Biochemistry AS CR, v.v.i., Flemingovo nam. 2, 166 10 Prague 6. Czech Republic
| | - Evzen Boura
- Institute of Organic Chemistry and Biochemistry AS CR, v.v.i., Flemingovo nam. 2, 166 10 Prague 6. Czech Republic
| | - Murali Aarthy
- Computer Aided Drug Design and Molecular Modeling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi-630004, Tamil Nadu. India
| | - Muthuraja Arun Pravin
- Computer Aided Drug Design and Molecular Modeling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi-630004, Tamil Nadu. India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modeling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi-630004, Tamil Nadu. India
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34
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Kaur H, Sarma P, Bhattacharyya A, Sharma S, Chhimpa N, Prajapat M, Prakash A, Kumar S, Singh A, Singh R, Avti P, Thota P, Medhi B. Efficacy and safety of dihydroorotate dehydrogenase (DHODH) inhibitors "leflunomide" and "teriflunomide" in Covid-19: A narrative review. Eur J Pharmacol 2021; 906:174233. [PMID: 34111397 PMCID: PMC8180448 DOI: 10.1016/j.ejphar.2021.174233] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 05/30/2021] [Accepted: 06/02/2021] [Indexed: 01/12/2023]
Abstract
Dihydroorotate dehydrogenase (DHODH) is rate-limiting enzyme in biosynthesis of pyrimidone which catalyzes the oxidation of dihydro-orotate to orotate. Orotate is utilized in the biosynthesis of uridine-monophosphate. DHODH inhibitors have shown promise as antiviral agent against Cytomegalovirus, Ebola, Influenza, Epstein Barr and Picornavirus. Anti-SARS-CoV-2 action of DHODH inhibitors are also coming up. In this review, we have reviewed the safety and efficacy of approved DHODH inhibitors (leflunomide and teriflunomide) against COVID-19. In target-centered in silico studies, leflunomide showed favorable binding to active site of MPro and spike: ACE2 interface. In artificial-intelligence/machine-learning based studies, leflunomide was among the top 50 ligands targeting spike: ACE2 interaction. Leflunomide is also found to interact with differentially regulated pathways [identified by KEGG (Kyoto Encyclopedia of Genes and Genomes) and reactome pathway analysis of host transcriptome data] in cogena based drug-repurposing studies. Based on GSEA (gene set enrichment analysis), leflunomide was found to target pathways enriched in COVID-19. In vitro, both leflunomide (EC50 41.49 ± 8.8 μmol/L) and teriflunomide (EC50 26 μmol/L) showed SARS-CoV-2 inhibition. In clinical studies, leflunomide showed significant benefit in terms of decreasing the duration of viral shredding, duration of hospital stay and severity of infection. However, no advantage was seen while combining leflunomide and IFN alpha-2a among patients with prolonged post symptomatic viral shredding. Common adverse effects of leflunomide were hyperlipidemia, leucopenia, neutropenia and liver-function alteration. Leflunomide/teriflunomide may serve as an agent of importance to achieve faster virological clearance in COVID-19, however, findings needs to be validated in bigger sized placebo controlled studies.
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Affiliation(s)
- Hardeep Kaur
- Department of Pharmacology, PGIMER, Chandigarh, India
| | - Phulen Sarma
- Department of Pharmacology, PGIMER, Chandigarh, India
| | | | | | | | | | - Ajay Prakash
- Department of Pharmacology, PGIMER, Chandigarh, India
| | - Subodh Kumar
- Department of Pharmacology, PGIMER, Chandigarh, India
| | | | - Rahul Singh
- Department of Pharmacology, PGIMER, Chandigarh, India
| | - Pramod Avti
- Department of Biophysics, PGIMER, Chandigarh, India
| | - Prasad Thota
- Department of Pharmacology, PGIMER, Chandigarh, India
| | - Bikash Medhi
- Department of Pharmacology, PGIMER, Chandigarh, India.
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35
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An Overview of COVID-19 and the Potential Plant Harboured Secondary Metabolites against SARS-CoV-2: A Review. JOURNAL OF PURE AND APPLIED MICROBIOLOGY 2021. [DOI: 10.22207/jpam.15.3.52] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The SARS-CoV-2 virus causes COVID-19, a pandemic disease, and it is called the novel coronavirus. It belongs to the Coronaviridae family and has been plagued the world since the end of 2019. Viral infection to the lungs causes fluid filling and breathing difficulties, which leads to pneumonia. Pneumonia progresses to ARDS (Acute Respiratory Distress Syndrome), in which fluid fills the air sac and seeps from the pulmonary veins. In the current scenario, several vaccines have been used to control the pandemic worldwide. Even though vaccines are available and their effectiveness is short, it may be helpful to curb the pandemic, but long-term protection is inevitable when we look for other options. Plants have diversified components such as primary and secondary metabolites. These molecules show several activities such as anti-microbial, anti-cancer, anti-helminthic. In addition, these molecules have good binding ability to the SARS-CoV-2 virus proteins such as RdRp (RNA-dependent RNA polymerase), Mpro (Main Protease), etc. Therefore, these herbal molecules could probably be used to control the COVID-19. However, pre-requisite tests, such as cytotoxicity, in vivo, and human experimental studies, are required before plant molecules can be used as potent drugs. Plant metabolites such as alkaloids, isoquinoline ß-carboline, and quinoline alkaloids such as skimmianine, quinine, cinchonine, and dictamine are present in plants and used in a traditional medicinal system.
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36
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Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller KR, Tkatchenko A. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems. Chem Rev 2021; 121:9816-9872. [PMID: 34232033 PMCID: PMC8391798 DOI: 10.1021/acs.chemrev.1c00107] [Citation(s) in RCA: 186] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Indexed: 12/23/2022]
Abstract
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
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Affiliation(s)
- John A. Keith
- Department
of Chemical and Petroleum Engineering Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Valentin Vassilev-Galindo
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Bingqing Cheng
- Accelerate
Programme for Scientific Discovery, Department
of Computer Science and Technology, 15 J. J. Thomson Avenue, Cambridge CB3 0FD, United Kingdom
| | - Stefan Chmiela
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Michael Gastegger
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Klaus-Robert Müller
- Machine
Learning Group, Technische Universität
Berlin, 10587, Berlin, Germany
- Department
of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea
- Max-Planck-Institut für Informatik, 66123 Saarbrücken, Germany
- Google Research, Brain Team, 10117 Berlin, Germany
| | - Alexandre Tkatchenko
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
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Veit-Acosta M, de Azevedo Junior WF. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery. Curr Med Chem 2021; 29:2438-2455. [PMID: 34365938 DOI: 10.2174/0929867328666210806105810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.
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Affiliation(s)
- Martina Veit-Acosta
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI 49008. United States
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Prasad K, Kumar V. Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2021; 2:100042. [PMID: 34870150 PMCID: PMC8317454 DOI: 10.1016/j.crphar.2021.100042] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022] Open
Abstract
It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.
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Affiliation(s)
- Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP, 201303, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP, 201303, India
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Moubarak M, Kasozi KI, Hetta HF, Shaheen HM, Rauf A, Al-kuraishy HM, Qusti S, Alshammari EM, Ayikobua ET, Ssempijja F, Afodun AM, Kenganzi R, Usman IM, Ochieng JJ, Osuwat LO, Matama K, Al-Gareeb AI, Kairania E, Musenero M, Welburn SC, Batiha GES. The Rise of SARS-CoV-2 Variants and the Role of Convalescent Plasma Therapy for Management of Infections. Life (Basel) 2021; 11:734. [PMID: 34440478 PMCID: PMC8399171 DOI: 10.3390/life11080734] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 02/07/2023] Open
Abstract
Novel therapies for the treatment of COVID-19 are continuing to emerge as the SARS-Cov-2 pandemic progresses. PCR remains the standard benchmark for initial diagnosis of COVID-19 infection, while advances in immunological profiling are guiding clinical treatment. The SARS-Cov-2 virus has undergone multiple mutations since its emergence in 2019, resulting in changes in virulence that have impacted on disease severity globally. The emergence of more virulent variants of SARS-Cov-2 remains challenging for effective disease control during this pandemic. Major variants identified to date include B.1.1.7, B.1.351; P.1; B.1.617.2; B.1.427; P.2; P.3; B.1.525; and C.37. Globally, large unvaccinated populations increase the risk of more and more variants arising. With successive waves of COVID-19 emerging, strategies that mitigate against community transmission need to be implemented, including increased vaccination coverage. For treatment, convalescent plasma therapy, successfully deployed during recent Ebola outbreaks and for H1N1 influenza, can increase survival rates and improve host responses to viral challenge. Convalescent plasma is rich with cytokines (IL-1β, IL-2, IL-6, IL-17, and IL-8), CCL2, and TNFα, neutralizing antibodies, and clotting factors essential for the management of SARS-CoV-2 infection. Clinical trials can inform and guide treatment policy, leading to mainstream adoption of convalescent therapy. This review examines the limited number of clinical trials published, to date that have deployed this therapy and explores clinical trials in progress for the treatment of COVID-19.
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Affiliation(s)
- Mohamed Moubarak
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, Egypt; (M.M.); (H.M.S.)
| | - Keneth Iceland Kasozi
- Infection Medicine, Deanery of Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, 1 George Square, Edinburgh EH8 9JZ, UK
- School of Medicine, Kabale University, Kabale P.O. Box 317, Uganda
| | - Helal F. Hetta
- Department of Medical Microbiology and Immunology, Faculty of Medicine, Assiut University, Assiut 71515, Egypt;
| | - Hazem M. Shaheen
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, Egypt; (M.M.); (H.M.S.)
| | - Abdur Rauf
- Department of Chemistry, University of Swabi, Swabi 23561, Pakistan;
| | - Hayder M. Al-kuraishy
- Department of Clinical Pharmacology and Medicine, College of Medicine, Al-Mustansiriyia University, P.O. Box 14022 Baghdad, Iraq;
| | - Safaa Qusti
- Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Eida M. Alshammari
- Department of Chemistry, College of Sciences, University of Ha’il, Ha’il 2440, Saudi Arabia;
| | - Emmanuel Tiyo Ayikobua
- School of Health Sciences, Soroti University, Soroti P.O. Box 211, Uganda; (E.T.A.); (L.O.O.)
| | - Fred Ssempijja
- Department of Anatomy, Faculty of Biomedical Sciences, Kampala International University, Western Campus, Bushenyi P.O. Box 71, Uganda; (F.S.); (I.M.U.); (J.J.O.)
| | - Adam Moyosore Afodun
- Department of Anatomy and Cell Biology, Faculty of Health Sciences, Busitema University, Tororo P.O. Box 236, Uganda; (A.M.A.); (E.K.)
| | - Ritah Kenganzi
- Department of Medical Laboratory Sciences, School of Allied Health Sciences, Kampala International University Teaching Hospital, Bushenyi P.O. Box 71, Uganda;
| | - Ibe Michael Usman
- Department of Anatomy, Faculty of Biomedical Sciences, Kampala International University, Western Campus, Bushenyi P.O. Box 71, Uganda; (F.S.); (I.M.U.); (J.J.O.)
| | - Juma John Ochieng
- Department of Anatomy, Faculty of Biomedical Sciences, Kampala International University, Western Campus, Bushenyi P.O. Box 71, Uganda; (F.S.); (I.M.U.); (J.J.O.)
| | - Lawrence Obado Osuwat
- School of Health Sciences, Soroti University, Soroti P.O. Box 211, Uganda; (E.T.A.); (L.O.O.)
| | - Kevin Matama
- School of Pharmacy, Kampala International University, Western Campus, Bushenyi P.O. Box 71, Uganda;
| | - Ali I. Al-Gareeb
- Department of Pharmacology, Toxicology and Medicine, College of Medicine Al-Mustansiriya University, Baghdad P.O. Box 14022, Iraq;
| | - Emmanuel Kairania
- Department of Anatomy and Cell Biology, Faculty of Health Sciences, Busitema University, Tororo P.O. Box 236, Uganda; (A.M.A.); (E.K.)
| | - Monica Musenero
- Ministry of Science Technology and Innovations, Government of Uganda, Kampala P.O. Box 7466, Uganda;
| | - Susan Christina Welburn
- Infection Medicine, Deanery of Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, 1 George Square, Edinburgh EH8 9JZ, UK
- Zhejiang University-University of Edinburgh Joint Institute, Zhejiang University, International Campus, 718 East Haizhou Road, Haining 314400, China
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, Egypt; (M.M.); (H.M.S.)
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Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay. Proc Natl Acad Sci U S A 2021; 118:2024302118. [PMID: 34234012 PMCID: PMC8325362 DOI: 10.1073/pnas.2024302118] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Recent spread of SARS-CoV-2 has sparked significant health concerns of emerging infectious viruses. Drug repurposing is a tangible strategy for developing antiviral agents within a short period. In general, drug repurposing starts with virtual screening of approved drugs employing docking simulations. However, the actual hit rate is low, and most of the predicted compounds are false positives. To tackle the challenges, we report advanced virtual screening with pre- and postdocking pharmacophore filtering of 6,218 drugs for COVID-19. Notably, 7 out of 38 compounds showed efficacies in inhibiting SARS-CoV-2 in Vero cells. Three of these were also found to inhibit SARS-CoV-2 in human Calu-3 cells. Furthermore, three drug combinations showed strong synergistic effects in SARS-CoV-2 inhibition at their clinically achievable concentrations. The COVID-19 pandemic caused by SARS-CoV-2 is an unprecedentedly significant health threat, prompting the need for rapidly developing antiviral drugs for the treatment. Drug repurposing is currently one of the most tangible options for rapidly developing drugs for emerging and reemerging viruses. In general, drug repurposing starts with virtual screening of approved drugs employing various computational methods. However, the actual hit rate of virtual screening is very low, and most of the predicted compounds are false positives. Here, we developed a strategy for virtual screening with much reduced false positives through incorporating predocking filtering based on shape similarity and postdocking filtering based on interaction similarity. We applied this advanced virtual screening approach to repurpose 6,218 approved and clinical trial drugs for COVID-19. All 6,218 compounds were screened against main protease and RNA-dependent RNA polymerase of SARS-CoV-2, resulting in 15 and 23 potential repurposed drugs, respectively. Among them, seven compounds can inhibit SARS-CoV-2 replication in Vero cells. Three of these drugs, emodin, omipalisib, and tipifarnib, show anti-SARS-CoV-2 activities in human lung cells, Calu-3. Notably, the activity of omipalisib is 200-fold higher than that of remdesivir in Calu-3. Furthermore, three drug combinations, omipalisib/remdesivir, tipifarnib/omipalisib, and tipifarnib/remdesivir, show strong synergistic effects in inhibiting SARS-CoV-2. Such drug combination therapy improves antiviral efficacy in SARS-CoV-2 infection and reduces the risk of each drug’s toxicity. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating COVID-19 and other viruses.
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El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
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Surianarayanan C, Chelliah PR. Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment. NEW GENERATION COMPUTING 2021; 39:717-741. [PMID: 34131359 PMCID: PMC8191724 DOI: 10.1007/s00354-021-00128-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/05/2021] [Indexed: 05/15/2023]
Abstract
The Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2). Most of the people do not have the acquired immunity to fight this virus. There is no specific treatment or medicine to cure the disease. The effects of this disease appear to vary from individual to individual, right from mild cough, fever to respiratory disease. It also leads to mortality in many people. As the virus has a very rapid transmission rate, the entire world is in distress. The control and prevention of this disease has evolved as an urgent and critical issue to be addressed through technological solutions. The Healthcare industry therefore needs support from the domain of artificial intelligence (AI). AI has the inherent capability of imitating the human brain and assisting in decision-making support by automatically learning from input data. It can process huge amounts of data quickly without getting tiresome and making errors. AI technologies and tools significantly relieve the burden of healthcare professionals. In this paper, we review the critical role of AI in responding to different research challenges around the COVID-19 crisis. A sample implementation of a powerful probabilistic machine learning (ML) algorithm for assessment of risk levels of individuals is incorporated in this paper. Other pertinent application areas such as surveillance of people and hotspots, mortality prediction, diagnosis, prognostic assistance, drug repurposing and discovery of protein structure, and vaccine are presented. The paper also describes various challenges that are associated with the implementation of AI-based tools and solutions for practical use.
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Affiliation(s)
- Chellammal Surianarayanan
- Government Arts and Science College (Formerly Bharathidasan University Constituent Arts and Science College), Affiliated to Bharathidasan University, Tiruchirappalli, Tamilnadu India
| | - Pethuru Raj Chelliah
- Site Reliability Engineering Division, Reliance Jio Platforms Ltd, Bangalore, India
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Sharma A, Baldi A, Kumar Sharma D. How to spot COVID-19 patients: Speech & sound audio analysis for preliminary diagnosis of SARS-COV-2 corona patients. Int J Clin Pract 2021; 75:e14134. [PMID: 33683774 PMCID: PMC8250077 DOI: 10.1111/ijcp.14134] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/02/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND The global cases of COVID-19 increasing day by day. On 25 November 2020, a total of 59 850 910 cases reported globally with a 1 411 216 global death. In India, total cases in the country now stand at 91 77 841 including 86 04 955 recoveries and 4 38 667 active cases as on 24 November 2020, as per the data issued by ICMR. A new generation of voice/audio analysis application can tell whether the person is suffering from COVID-19 or not. AIMS To describe how to established a new generation of voice/audio analysis application to identify the suspected COVID-19 hidden cases in hotspot areas with the help of an audio sample of the general public. MATERIALS & METHODS The different patents and data available as literature on the internet are evaluated to make a new generation of voice/audio analysis application with the help of an audio sample of the general public. RESULTS The collection of the audio sample will be done from the already suffered COVID-19 patients in (.Wave files) personally or through phone calls. The audio samples such as the sound of the cough, the pattern of breathing, respiration rate and way of speech will be recorded. The parameters will be evaluated for loudness, articulation, tempo, rhythm, melody and timbre. The analysis and interpretation of the parameters can be made through machine learning and artificial intelligence to detect corona cases with an audio sample. DISCUSSION The voice/audio application current project can be merged with a mobile App called 'AarogyaSetu' by the Government of India. The project can be implemented in the high-risk area of COVID-19 in the country. CONCLUSION This new method of detecting cases will decrease the workload in the COVID-19 laboratory.
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Affiliation(s)
- Amit Sharma
- Department of Pharmacy PracticeISF College of PharmacyMogaPunjabIndia
- Uttarakhand Technical UniversityDehradunUttarakhandIndia
| | - Ashish Baldi
- Department of Pharmaceutical Science and TechnologyMaharaja Ranjit Singh Punjab Technical UniversityBathindaPunjabIndia
| | - Dinesh Kumar Sharma
- Department of PharmaceuticsHimalayan Institute of Pharmacy and ResearchUttarakhandIndia
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Ngo ST, Tam NM, Pham MQ, Nguyen TH. Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV-2 Mpro. J Chem Inf Model 2021; 61:2302-2312. [PMID: 33829781 PMCID: PMC8043216 DOI: 10.1021/acs.jcim.1c00159] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Indexed: 12/13/2022]
Abstract
The COVID-19 pandemic has killed millions of people worldwide since its outbreak in December 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, developing an effective therapy is an urgent task, which requires accurately estimating the ligand-binding free energy to SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with the experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The molecular docking approach was manipulated using Autodock Vina (Vina) and Autodock4 (AD4) protocols to preliminarily investigate the ligand-binding affinity and pose to SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4, and for free energy methods, FEP is the most accurate method, followed by LIE, FPL, and MM-PBSA (FEP > LIE ≈ FPL > MM-PBSA). Moreover, atomistic simulations revealed that the van der Waals interaction is the dominant factor. The residues Thr26, His41, Ser46, Asn142, Gly143, Cys145, His164, Glu166, and Gln189 are essential elements affecting the binding process. Our benchmark provides guidelines for further investigations using computational approaches.
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Affiliation(s)
- Son Tung Ngo
- Laboratory of Theoretical and Computational
Biophysics, Ton Duc Thang University, Ho Chi Minh City 700000,
Vietnam
- Faculty of Applied Sciences, Ton Duc
Thang University, Ho Chi Minh City 700000,
Vietnam
| | - Nguyen Minh Tam
- Faculty of Applied Sciences, Ton Duc
Thang University, Ho Chi Minh City 700000,
Vietnam
- Computional Chemistry Research Group, Ton
Duc Thang University, Ho Chi Minh City 700000,
Vietnam
| | - Minh Quan Pham
- Graduate University of Science and Technology,
Vietnam Academy of Science and Technology, Hanoi 100000,
Vietnam
- Institute of Natural Products Chemistry,
Vietnam Academy of Science and Technology, Hanoi 100000,
Vietnam
| | - Trung Hai Nguyen
- Laboratory of Theoretical and Computational
Biophysics, Ton Duc Thang University, Ho Chi Minh City 700000,
Vietnam
- Faculty of Applied Sciences, Ton Duc
Thang University, Ho Chi Minh City 700000,
Vietnam
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Srinivasan S, Batra R, Chan H, Kamath G, Cherukara MJ, Sankaranarayanan SKRS. Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19. ACS OMEGA 2021; 6:12557-12566. [PMID: 34056406 PMCID: PMC8154149 DOI: 10.1021/acsomega.1c00477] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 02/18/2021] [Indexed: 05/14/2023]
Abstract
An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the in silico ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a de novo molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several (∼100's) new therapeutic agents that outperform Food and Drug Administration (FDA) (∼1000's) and non-FDA molecules (∼million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality.
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Affiliation(s)
- Srilok Srinivasan
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
| | - Rohit Batra
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
| | - Henry Chan
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
- Department
of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
| | - Ganesh Kamath
- Dalzielfiver
LLC, 3500 Carlfield Street, El Sobrante, California 94803, United States
| | - Mathew J. Cherukara
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
- Advanced
Photon Source, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Subramanian K. R. S. Sankaranarayanan
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
- Department
of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
- , . Phone: +1 (312) 355-6770
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Yokoyama K, Ichiki A. Nano-size dependence in the adsorption by the SARS-CoV-2 spike protein over gold colloid. Colloids Surf A Physicochem Eng Asp 2021; 615:126275. [PMID: 33564211 PMCID: PMC7860964 DOI: 10.1016/j.colsurfa.2021.126275] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/31/2020] [Accepted: 01/29/2021] [Indexed: 12/28/2022]
Abstract
Gold nano-particles were coated with the spike protein (S protein) of SARS-CoV-2 and exposed to increasingly acidic conditions. Their responses were investigated by monitoring the surface plasmon resonance (SPR) band shift. As the external pH was gradually changed from neutral pH to pH ∼2 the peak of the SPR band showed a significant red-shift, with a sigmoidal feature implying the formation of the gold-protein aggregates. The coating of S protein changed the surface property of the gold enough to extract the coverage fraction of protein over nano particles, Θ, which did not exhibit clear nano-size dependence. The geometrical simulation to explain Θ showed the average axial length to be a = 7. 25 nm and b =8.00 nm when the S-protein was hypothesized as a prolate shape with spiking-out orientation. As the pH value externally hopped between pH∼3 and pH∼10, a behavior of reversible protein folding was observed for particles with diameters >30 nm. It was concluded that S protein adsorption conformation was impacted by the size (diameter, d) of a core nano-gold, where head-to-head dimerized S protein was estimated for d ≤ 80 nm and a parallel in opposite directions formation for d = 100 nm.
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Affiliation(s)
- Kazushige Yokoyama
- Department of Chemistry, The State University of New York Geneseo College, Geneseo, NY, United States
| | - Akane Ichiki
- Department of Chemistry, The State University of New York Geneseo College, Geneseo, NY, United States
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Lam C, Siefkas A, Zelin NS, Barnes G, Dellinger RP, Vincent JL, Braden G, Burdick H, Hoffman J, Calvert J, Mao Q, Das R. Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids. Clin Ther 2021; 43:871-885. [PMID: 33865643 PMCID: PMC8006198 DOI: 10.1016/j.clinthera.2021.03.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/01/2021] [Accepted: 03/21/2021] [Indexed: 12/15/2022]
Abstract
Purpose: Coronavirus disease–2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time. Methods: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment. Findings: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04). Implications: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.
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Affiliation(s)
| | | | | | | | - R Phillip Dellinger
- Division of Critical Care Medicine, Cooper University Hospital/Cooper Medical School, Rowan University, Camden, New Jersey
| | - Jean-Louis Vincent
- Department of Intensive Care, Erasme University Hospital, Université Libre, Brussels, Belgium
| | - Gregory Braden
- Kidney Care and Transplant Associates of New England, Springfield, Massachusetts
| | - Hoyt Burdick
- Cabell Huntington Hospital, Huntington, West Virginia; School of Medicine, Marshall University, Huntington, West Virginia
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48
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Cuesta SA, Mora JR, Márquez EA. In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2. Molecules 2021; 26:1100. [PMID: 33669720 PMCID: PMC7923184 DOI: 10.3390/molecules26041100] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/11/2021] [Accepted: 02/16/2021] [Indexed: 12/29/2022] Open
Abstract
Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.
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Affiliation(s)
- Sebastián A. Cuesta
- Grupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Colegio Politécnico, Universidad San Francisco de Quito, Diego de Robles y Vía Interoceánica, Quito 170901, Ecuador;
| | - José R. Mora
- Grupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Colegio Politécnico, Universidad San Francisco de Quito, Diego de Robles y Vía Interoceánica, Quito 170901, Ecuador;
| | - Edgar A. Márquez
- Grupo de Investigaciones en Química y Biología, Departamento de Química y Biología, Facultad de Ciencias Exactas, Universidad del Norte, Carrera 51B, Km 5, vía Puerto Colombia, Barranquilla 081007, Colombia
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49
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Watashi K. Identifying and repurposing antiviral drugs against severe acute respiratory syndrome coronavirus 2 with in silico and in vitro approaches. Biochem Biophys Res Commun 2021; 538:137-144. [PMID: 33272566 PMCID: PMC7678433 DOI: 10.1016/j.bbrc.2020.10.094] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 10/27/2020] [Indexed: 01/18/2023]
Abstract
Coronavirus infectious diseases 2019 (COVID-19), a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a serious public health threat worldwide. So far, there are no drugs and vaccines whose efficacy has been well-proven. After the outbreak, there has been a massive search for anti-SARS-CoV-2 medications, focusing on approved drugs because repurposing approved drugs will take less time to reach clinical usage than new drugs. This article summarizes the studies using in silico and in vitro approaches to identify therapeutic candidates among approved drugs that target the SARS-CoV-2 life cycle.
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Affiliation(s)
- Koichi Watashi
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, 162-8640, Japan,Department of Applied Biological Sciences, Tokyo University of Science, Noda, 278-8510, Japan,Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan,MIRAI, JST, Saitama, 332-0012, Japan,Department of Virology II, National Institute of Infectious Diseases, 1-23-1 Toyama, Shinjuku-ku, Tokyo, 162-8640, Japan
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50
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Tayarani N MH. Applications of artificial intelligence in battling against covid-19: A literature review. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110338. [PMID: 33041533 PMCID: PMC7532790 DOI: 10.1016/j.chaos.2020.110338] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/01/2020] [Indexed: 05/14/2023]
Abstract
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
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Affiliation(s)
- Mohammad-H Tayarani N
- Biocomputation Group, School of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
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