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Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today 2024; 29:103992. [PMID: 38663579 DOI: 10.1016/j.drudis.2024.103992] [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] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 04/18/2024] [Indexed: 05/04/2024]
Abstract
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule 424001, Maharashtra, India
| | - Antonio Lavecchia
- "Drug Discovery" Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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2
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Monsia R, Bhattacharyya S. Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique. RESEARCH SQUARE 2024:rs.3.rs-4355625. [PMID: 38766198 PMCID: PMC11100899 DOI: 10.21203/rs.3.rs-4355625/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it. Scientific contribution We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery.
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3
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Fang N, Wu L, Duan S, Li J. The Structural and Molecular Mechanisms of Mycobacterium tuberculosis Translational Elongation Factor Proteins. Molecules 2024; 29:2058. [PMID: 38731549 PMCID: PMC11085428 DOI: 10.3390/molecules29092058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
Targeting translation factor proteins holds promise for developing innovative anti-tuberculosis drugs. During protein translation, many factors cause ribosomes to stall at messenger RNA (mRNA). To maintain protein homeostasis, bacteria have evolved various ribosome rescue mechanisms, including the predominant trans-translation process, to release stalled ribosomes and remove aberrant mRNAs. The rescue systems require the participation of translation elongation factor proteins (EFs) and are essential for bacterial physiology and reproduction. However, they disappear during eukaryotic evolution, which makes the essential proteins and translation elongation factors promising antimicrobial drug targets. Here, we review the structural and molecular mechanisms of the translation elongation factors EF-Tu, EF-Ts, and EF-G, which play essential roles in the normal translation and ribosome rescue mechanisms of Mycobacterium tuberculosis (Mtb). We also briefly describe the structure-based, computer-assisted study of anti-tuberculosis drugs.
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Affiliation(s)
- Ning Fang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai 200438, China; (N.F.); (L.W.)
| | - Lingyun Wu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai 200438, China; (N.F.); (L.W.)
| | - Shuyan Duan
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai 200438, China; (N.F.); (L.W.)
- College of Food Science and Pharmaceutical Engineering, Zaozhuang University, Zaozhuang 277160, China
| | - Jixi Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai 200438, China; (N.F.); (L.W.)
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4
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Mauri A, Bertola M. AlvaBuilder: A Software for De Novo Molecular Design. J Chem Inf Model 2024; 64:2136-2142. [PMID: 37399048 PMCID: PMC11005826 DOI: 10.1021/acs.jcim.3c00610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Indexed: 07/04/2023]
Abstract
AlvaBuilder is a software tool for de novo molecular design and can be used to generate novel molecules having desirable characteristics. Such characteristics can be defined using a simple step by step graphical interface, and they can be based on molecular descriptors, on predictions of QSAR/QSPR models, and on matching molecular fragments or used to design compounds similar to a given one. The molecules generated are always syntactically valid since they are composed by combining fragments of molecules taken from a training data set chosen by the user. In this paper, we demonstrate how the software can be used to design new compounds for a defined case study. AlvaBuilder is available at https://www.alvascience.com/alvabuilder/.
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Affiliation(s)
- Andrea Mauri
- Alvascience
Srl, Via Giuseppe Parini,
35, 23900 Lecco, Italy
| | - Matteo Bertola
- Alvascience
Srl, Via Giuseppe Parini,
35, 23900 Lecco, Italy
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5
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Cieślak M, Danel T, Krzysztyńska-Kuleta O, Kalinowska-Tłuścik J. Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors. Sci Rep 2024; 14:8228. [PMID: 38589405 DOI: 10.1038/s41598-024-58122-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/26/2024] [Indexed: 04/10/2024] Open
Abstract
Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.
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Affiliation(s)
- Marcin Cieślak
- Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387, Kraków, Małopolska, Poland.
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Prof. S. Łojasiewicza 11, 30-348, Kraków, Małopolska, Poland.
- Computational Chemistry Department, Selvita, Bobrzynskiego 14, 30-348, Kraków, Małopolska, Poland.
| | - Tomasz Danel
- Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387, Kraków, Małopolska, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Prof. S. Łojasiewicza 6, 30-348, Kraków, Małopolska, Poland
| | - Olga Krzysztyńska-Kuleta
- Cell and Molecular Biology Department, Selvita, Bobrzynskiego 14, 30-348, Kraków, Małopolska, Poland
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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7
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Bhatnagar A, Nath V, Kumar N, Kumar V. Discovery of novel PARP-1 inhibitors using tandem in silico studies: integrated docking, e-pharmacophore, deep learning based de novo and molecular dynamics simulation approach. J Biomol Struct Dyn 2024; 42:3396-3409. [PMID: 37216358 DOI: 10.1080/07391102.2023.2214223] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/05/2023] [Indexed: 05/24/2023]
Abstract
Cancer accounts for the majority of deaths worldwide, and the increasing incidence of breast cancer is a matter of grave concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as an attractive target for the treatment of breast cancer as it has an important role in DNA repair. The focus of the study was to identify novel PARP-1 inhibitors using a blend of tandem structure-based screening (Docking and e-pharmacophore-based screening) and artificial intelligence (deep learning)-based de novo approaches. The scrutiny of compounds having good binding characteristics for PARP-1 was carried out using a tandem mode of screening along with parameters such as binding energy and ADME analysis. The efforts afforded compound Vab1 (PubChem ID 129142036), which was chosen as a seed for obtaining novel compounds through a trained artificial intelligence (AI)-based model. Resultant compounds were assessed for PARP-1 inhibition; binding affinity prediction and interaction pattern analysis were carried out using the extra precision (XP) mode of docking. Two best hits, Vab1-b and Vab1-g, exhibiting good dock scores and suitable interactions, were subjected to 100 nanoseconds (ns) of molecular dynamics simulation in the active site of PARP-1 and compared with the reference Protein-Ligand Complex. The stable nature of PARP-1 upon binding to these compounds was revealed through MD simulation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Aayushi Bhatnagar
- Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Ajmer, India
| | - Virendra Nath
- Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Ajmer, India
| | - Neeraj Kumar
- Bhupal Nobles' College of Pharmacy, Bhupal Nobles' University, Udaipur, India
| | - Vipin Kumar
- Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Ajmer, India
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8
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Tang Y, Moretti R, Meiler J. Recent Advances in Automated Structure-Based De Novo Drug Design. J Chem Inf Model 2024; 64:1794-1805. [PMID: 38485516 DOI: 10.1021/acs.jcim.4c00247] [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: 03/26/2024]
Abstract
As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.
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Affiliation(s)
- Yidan Tang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany
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9
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Sankar S, Vasudevan S, Chandra N. CRD: A de novo design algorithm for the prediction of cognate protein receptors for small molecule ligands. Structure 2024; 32:362-375.e4. [PMID: 38194962 DOI: 10.1016/j.str.2023.12.009] [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] [Received: 06/21/2023] [Revised: 10/20/2023] [Accepted: 12/13/2023] [Indexed: 01/11/2024]
Abstract
While predicting a ligand that binds to a protein is feasible with current methods, the opposite, i.e., the prediction of a receptor for a ligand remains challenging. We present an approach for predicting receptors of a given ligand that uses de novo design and structural bioinformatics. We have developed the algorithm CRD, comprising multiple modules combining fragment-based sub-site finding, a machine learning function to estimate the size of the site, a genetic algorithm that encodes knowledge on protein structures and a physics-based fitness scoring scheme. CRD includes a pseudo-receptor design component followed by a mapping component to identify proteins that might contain these sites. CRD recovers the sites and receptors of several natural ligands. It designs similar sites for similar ligands, yet to some extent can distinguish between closely related ligands. CRD correctly predicts receptor classes for several drugs and might become a valuable tool for drug discovery.
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Affiliation(s)
- Santhosh Sankar
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Sneha Vasudevan
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India; Department of Bioengineering, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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10
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Cebi E, Lee J, Subramani VK, Bak N, Oh C, Kim KK. Cryo-electron microscopy-based drug design. Front Mol Biosci 2024; 11:1342179. [PMID: 38501110 PMCID: PMC10945328 DOI: 10.3389/fmolb.2024.1342179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/31/2024] [Indexed: 03/20/2024] Open
Abstract
Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.
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Affiliation(s)
| | | | | | | | - Changsuk Oh
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Kyeong Kyu Kim
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
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11
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Napolitano G, Has C, Schwerk A, Yuan JH, Ullrich C. Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases. Pharmaceut Med 2024; 38:79-86. [PMID: 38315404 DOI: 10.1007/s40290-023-00504-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 02/07/2024]
Abstract
The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.
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Affiliation(s)
| | - Canan Has
- Centogene GmbH, Alboinstraße 36-42, 12103, Berlin, Germany
| | - Anne Schwerk
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jui-Hung Yuan
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Ullrich
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
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12
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He D, Liu Q, Mi Y, Meng Q, Xu L, Hou C, Wang J, Li N, Liu Y, Chai H, Yang Y, Liu J, Wang L, Hou Y. De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307245. [PMID: 38204214 PMCID: PMC10962488 DOI: 10.1002/advs.202307245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/05/2023] [Indexed: 01/12/2024]
Abstract
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.
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Affiliation(s)
- Dakuo He
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Qing Liu
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Yan Mi
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Qingqi Meng
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Libin Xu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Chunyu Hou
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Jinpeng Wang
- College of Information Science and EngineeringState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang110819China
| | - Ning Li
- School of Traditional Chinese Materia MedicaKey Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang CityShenyang Pharmaceutical UniversityShenyang110016China
| | - Yang Liu
- Key Laboratory of Structure‐Based Drug Design & Discovery of Ministry of EducationShenyang Pharmaceutical UniversityShenyang110016China
| | - Huifang Chai
- School of PharmacyGuizhou University of Traditional Chinese MedicineGuiyang550025China
| | - Yanqiu Yang
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Jingyu Liu
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
| | - Lihui Wang
- Department of PharmacologyShenyang Pharmaceutical UniversityShenyang110016China
| | - Yue Hou
- Key Laboratory of Bioresource Research and Development of Liaoning ProvinceCollege of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationNortheastern UniversityShenyang110169China
- Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang110169China
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13
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Loeffler HH, He J, Tibo A, Janet JP, Voronov A, Mervin LH, Engkvist O. Reinvent 4: Modern AI-driven generative molecule design. J Cheminform 2024; 16:20. [PMID: 38383444 PMCID: PMC10882833 DOI: 10.1186/s13321-024-00812-5] [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: 11/23/2023] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open-source reference implementation for generative molecular design where the software is also being used in production to support in-house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.
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Affiliation(s)
- Hannes H Loeffler
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
| | - Jiazhen He
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Alessandro Tibo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Lewis H Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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14
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Nigam A, Pollice R, Friederich P, Aspuru-Guzik A. Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network. Chem Sci 2024; 15:2618-2639. [PMID: 38362419 PMCID: PMC10866360 DOI: 10.1039/d3sc05306g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 01/10/2024] [Indexed: 02/17/2024] Open
Abstract
The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this space, classical workflows rely on the domain knowledge and creativity of human experts, which can be the bottleneck in high-throughput approaches. Herein, we present an artificial molecular design workflow relying on a genetic algorithm and a deep neural network to find a new family of organic emitters with inverted singlet-triplet gaps and appreciable fluorescence rates. We combine high-throughput virtual screening and inverse design infused with domain knowledge and artificial intelligence to accelerate molecular generation significantly. This enabled us to explore more than 800 000 potential emitter molecules and find more than 10 000 candidates estimated to have inverted singlet-triplet gaps (INVEST) and appreciable fluorescence rates, many of which likely emit blue light. This class of molecules has the potential to realize a new generation of organic light-emitting diodes.
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Affiliation(s)
- AkshatKumar Nigam
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto 80 St. George St Toronto Ontario M5S 3H6 Canada
- Department of Computer Science, University of Toronto 40 St. George St Toronto Ontario M5S 2E4 Canada
| | - Robert Pollice
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto 80 St. George St Toronto Ontario M5S 3H6 Canada
- Department of Computer Science, University of Toronto 40 St. George St Toronto Ontario M5S 2E4 Canada
| | - Pascal Friederich
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto 80 St. George St Toronto Ontario M5S 3H6 Canada
- Department of Computer Science, University of Toronto 40 St. George St Toronto Ontario M5S 2E4 Canada
- Institute of Nanotechnology, Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology Am Fasanengarten 5 76131 Karlsruhe Germany
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto 80 St. George St Toronto Ontario M5S 3H6 Canada
- Department of Computer Science, University of Toronto 40 St. George St Toronto Ontario M5S 2E4 Canada
- Vector Institute for Artificial Intelligence 661 University Ave Suite 710 Toronto Ontario M5G 1M1 Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto 200 College St. Ontario M5S 3E5 Canada
- Department of Materials Science & Engineering, University of Toronto, 184 College St. Ontario M5S 3E4 Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR) 661 University Ave Toronto Ontario M5G Canada
- Acceleration Consortium Toronto Ontario M5G 3H6 Canada
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15
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Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15:1331062. [PMID: 38384298 PMCID: PMC10879372 DOI: 10.3389/fphar.2024.1331062] [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: 11/07/2023] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Azim Ansari
- Computer Aided Drug Design Center Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, Malaysia
| | - Vinoth Kumarasamy
- Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai, Malaysia
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16
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Ang D, Rakovski C, Atamian HS. De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search. Pharmaceuticals (Basel) 2024; 17:161. [PMID: 38399376 PMCID: PMC10892138 DOI: 10.3390/ph17020161] [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: 10/30/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.
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Affiliation(s)
- Dony Ang
- Computational and Data Sciences Program, Chapman University, Orange, CA 92866, USA; (D.A.); (C.R.)
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Cyril Rakovski
- Computational and Data Sciences Program, Chapman University, Orange, CA 92866, USA; (D.A.); (C.R.)
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Hagop S. Atamian
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
- Biological Sciences Program, Chapman University, Orange, CA 92866, USA
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17
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Sunildutt N, Ahmed F, Chethikkattuveli Salih AR, Lim JH, Choi KH. Integrating Transcriptomic and Structural Insights: Revealing Drug Repurposing Opportunities for Sporadic ALS. ACS OMEGA 2024; 9:3793-3806. [PMID: 38284068 PMCID: PMC10809234 DOI: 10.1021/acsomega.3c07296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 01/30/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive and devastating neurodegenerative disorder characterized by the loss of upper and lower motor neurons, resulting in debilitating muscle weakness and atrophy. Currently, there are no effective treatments available for ALS, posing significant challenges in managing the disease that affects approximately two individuals per 100,000 people annually. To address the urgent need for effective ALS treatments, we conducted a drug repurposing study using a combination of bioinformatics tools and molecular docking techniques. We analyzed sporadic ALS-related genes from the GEO database and identified key signaling pathways involved in sporadic ALS pathogenesis through pathway analysis using DAVID. Subsequently, we utilized the Clue Connectivity Map to identify potential drug candidates and performed molecular docking using AutoDock Vina to evaluate the binding affinity of short-listed drugs to key sporadic ALS-related genes. Our study identified Cefaclor, Diphenidol, Flubendazole, Fluticasone, Lestaurtinib, Nadolol, Phenamil, Temozolomide, and Tolterodine as potential drug candidates for repurposing in sporadic ALS treatment. Notably, Lestaurtinib demonstrated high binding affinity toward multiple proteins, suggesting its potential as a broad-spectrum therapeutic agent for sporadic ALS. Additionally, docking analysis revealed NOS3 as the gene that interacts with all the short-listed drugs, suggesting its possible involvement in the mechanisms underlying the therapeutic potential of these drugs in sporadic ALS. Overall, our study provides a systematic framework for identifying potential drug candidates for sporadic ALS therapy and highlights the potential of drug repurposing as a promising strategy for discovering new therapies for neurodegenerative diseases.
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Affiliation(s)
- Naina Sunildutt
- Department
of Mechatronics Engineering, Jeju National
University, Jeju63243, Republic
of Korea
| | - Faheem Ahmed
- Department
of Mechatronics Engineering, Jeju National
University, Jeju63243, Republic
of Korea
| | - Abdul Rahim Chethikkattuveli Salih
- Department
of Mechatronics Engineering, Jeju National
University, Jeju63243, Republic
of Korea
- Terasaki
Institute for Biomedical InnovationLos Angeles21100, United States
| | - Jong Hwan Lim
- Department
of Mechatronics Engineering, Jeju National
University, Jeju63243, Republic
of Korea
| | - Kyung Hyun Choi
- Department
of Mechatronics Engineering, Jeju National
University, Jeju63243, Republic
of Korea
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18
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Lin S, Chen W, Alqahtani MS, Elkamchouchi DH, Ge Y, Lu Y, Zhang G, Wang M. Exploring the therapeutic potential of layered double hydroxides and transition metal dichalcogenides through the convergence of rheumatology and nanotechnology using generative adversarial network. ENVIRONMENTAL RESEARCH 2024; 241:117262. [PMID: 37839531 DOI: 10.1016/j.envres.2023.117262] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/10/2023] [Accepted: 09/27/2023] [Indexed: 10/17/2023]
Abstract
Two-dimensional Layered double hydroxides (LDHs) are highly used in the biomedical domain due to their biocompatibility, biodegradability, controlled drug loading and release capabilities, and improved cellular permeability. The interaction of LDHs with biological systems could facilitate targeted drug delivery and make them an attractive option for various biomedical applications. Rheumatoid Arthritis (RA) requires targeted drug delivery for optimum therapeutic outcomes. In this study, stacked double hydroxide nanocomposites with dextran sulphate modification (LDH-DS) were developed while exhibiting both targeting and pH-sensitivity for rheumatological conditions. This research examines the loading, release kinetics, and efficiency of the therapeutics of interest in the LDH-based drug delivery system. The mean size of LDH-DS particles (300.1 ± 8.12 nm) is -12.11 ± 0.4 mV. The encapsulation efficiency was 48.52%, and the loading efficacy was 16.81%. In vitro release tests indicate that the drug's discharge is modified more rapidly in PBS at pH 5.4 compared to pH 5.6, which later reached 7.3, showing the case sensitivity to pH. A generative adversarial network (GAN) is used to analyze the drug delivery system in rheumatology. The GAN model achieved high accuracy and classification rates of 99.3% and 99.0%, respectively, and a validity of 99.5%. The second and third administrations resulted in a significant change with p-values of 0.001 and 0.05, respectively. This investigation unequivocally demonstrated that LDH functions as a biocompatible drug delivery matrix, significantly improving delivery effectiveness.
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Affiliation(s)
- Suxian Lin
- Department of Rheumatology, Wenzhou People's Hospital, Wenzhou, 325000, China
| | - Weiwei Chen
- Department of Rheumatology, Wenzhou People's Hospital, Wenzhou, 325000, China
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia; BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE1 7RH, U.K
| | - Dalia H Elkamchouchi
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Yisu Ge
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325100, China
| | - Yanjie Lu
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guodao Zhang
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Mudan Wang
- Department of Nephrology, Wenzhou People's Hospital, Wenzhou, 325000, China.
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19
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Luo D, Liang Y, Yang Y, Wang X. Hybrid parameters for fluid identification using an enhanced quantum neural network in a tight reservoir. Sci Rep 2024; 14:1064. [PMID: 38212380 PMCID: PMC10784518 DOI: 10.1038/s41598-023-50455-z] [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: 03/28/2023] [Accepted: 12/20/2023] [Indexed: 01/13/2024] Open
Abstract
This paper proposes a fluid classifier for a tight reservoir using a quantum neural network (QNN). It is difficult to identify the fluid in tight reservoirs, and the manual interpretation of logging data, which is an important means to identify the fluid properties, has the disadvantages of a low recognition rate and non-intelligence, and an intelligent algorithm can better identify the fluid. For tight reservoirs, the logging response characteristics of different fluid properties and the sensitivity and relevance of well log parameter and rock physics parameters to fluid identification are analyzed, and different sets of input parameters for fluid identification are constructed. On the basis of quantum neural networks, a new method for combining sample quantum state descriptions, sensitivity analysis of input parameters, and wavelet activation functions for optimization is proposed. The results of identifying the dry layer, gas layer, and gas-water co-layer in the tight reservoir in the Sichuan Basin of China show that different input parameters and activation functions affect recognition performance. The proposed quantum neural network based on hybrid parameters and a wavelet activation function has higher fluid identification accuracy than the original quantum neural network model, indicating that this method is effective and warrants promotion and application.
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Affiliation(s)
- Dejiang Luo
- College of Mathematics and Physics, Chengdu University of Technology, Chengdu, 610059, China.
- Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, 610059, China.
| | - Yuan Liang
- Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, 610059, China
| | - Yuanjun Yang
- College of Mathematics and Physics, Chengdu University of Technology, Chengdu, 610059, China
| | - Xingyue Wang
- College of Mathematics and Physics, Chengdu University of Technology, Chengdu, 610059, China
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20
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Singh K, Bhushan B, Singh B. Advances in Drug Discovery and Design using Computer-aided Molecular Modeling. Curr Comput Aided Drug Des 2024; 20:697-710. [PMID: 37711101 DOI: 10.2174/1573409920666230914123005] [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] [Received: 07/04/2023] [Revised: 08/09/2023] [Accepted: 08/15/2023] [Indexed: 09/16/2023]
Abstract
Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.
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Affiliation(s)
- Kuldeep Singh
- Department of Pharmacology, Rajiv Academy for Pharmacy, Mathura Uttar Pradesh, India
| | - Bharat Bhushan
- Department of Pharmacology, Institute of Pharmaceutical Research, GLA University, Mathura Uttar Pradesh, India
| | - Bhoopendra Singh
- Department of Pharmacy, B.S.A. College of Engineering & Technology, Mathura Uttar Pradesh India
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21
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Olmedo DA, Durant-Archibold AA, López-Pérez JL, Medina-Franco JL. Design and Diversity Analysis of Chemical Libraries in Drug Discovery. Comb Chem High Throughput Screen 2024; 27:502-515. [PMID: 37409545 DOI: 10.2174/1386207326666230705150110] [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] [Received: 04/05/2023] [Revised: 05/30/2023] [Accepted: 05/30/2023] [Indexed: 07/07/2023]
Abstract
Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.
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Affiliation(s)
- Dionisio A Olmedo
- Centro de Investigaciones Farmacognósticas de la Flora Panameña (CIFLORPAN), Facultad de Farmacia, Universidad de Panamá, Ciudad de Panamá, Apartado, 0824-00178, Panamá
- Sistema Nacional de Investigación (SNI), Secretaria Nacional de Ciencia, Tecnología e Innovación (SENACYT), Ciudad del Saber, Clayton, Panamá
| | - Armando A Durant-Archibold
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Apartado, 0843-01103, Panamá
- Departamento de Bioquímica, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá
| | - José Luis López-Pérez
- CESIFAR, Departamento de Farmacología, Facultad de Medicina, Universidad de Panamá, Ciudad de Panamá, Panamá
- Departamento de Ciencias Farmacéuticas, Facultad de Farmacia, Universidad de Salamanca, Avda. Campo Charro s/n, 37071 Salamanca, España
| | - José Luis Medina-Franco
- DIFACQUIM Grupo de Investigación, Departamento de Farmacia, Escuela de Química, Universidad Nacional Autónoma de México, Ciudad de México, Apartado, 04510, México
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22
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Talevi A. Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects. Methods Mol Biol 2024; 2714:1-20. [PMID: 37676590 DOI: 10.1007/978-1-0716-3441-7_1] [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: 09/08/2023]
Abstract
Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.
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Affiliation(s)
- Alan Talevi
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata (UNLP), La Plata, Argentina.
- Argentinean National Council of Scientific and Technical Research (CONICET), La Plata, Argentina.
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23
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Tang Y, Zhao W, Zhu G, Tan Z, Huang L, Zhang P, Gao L, Rui Y. Nano-Pesticides and Fertilizers: Solutions for Global Food Security. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 14:90. [PMID: 38202545 PMCID: PMC10780761 DOI: 10.3390/nano14010090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 12/18/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024]
Abstract
Nanotechnology emerges as an important way to safeguard global food security amid the escalating challenges posed by the expansion of the global population and the impacts of climate change. The perfect fusion of this breakthrough technology with traditional agriculture promises to revolutionize the way agriculture is traditionally practiced and provide effective solutions to the myriad of challenges in agriculture. Particularly noteworthy are the applications of nano-fertilizers and pesticides in agriculture, which have become milestones in sustainable agriculture and offer lasting alternatives to traditional methods. This review meticulously explores the key role of nano-fertilizers and pesticides in advancing sustainable agriculture. By focusing on the dynamic development of nanotechnology in the field of sustainable agriculture and its ability to address the overarching issue of global food security, this review aims to shed light on the transformative potential of nanotechnology to pave the way for a more resilient and sustainable future for agriculture.
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Affiliation(s)
- Yuying Tang
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; (Y.T.); (G.Z.)
| | - Weichen Zhao
- State Key Laboratory for Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; (W.Z.); (Z.T.)
| | - Guikai Zhu
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; (Y.T.); (G.Z.)
| | - Zhiqiang Tan
- State Key Laboratory for Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; (W.Z.); (Z.T.)
| | - Lili Huang
- Jiaer Chen Academician Workstation, Jinan Huaxin Automation Engineering Co., Ltd., Xincheng Road, Shanghe County, Jinan 251616, China;
| | - Peng Zhang
- Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China;
| | - Li Gao
- State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yukui Rui
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; (Y.T.); (G.Z.)
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24
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Angelo JS, Guedes IA, Barbosa HJC, Dardenne LE. Multi-and many-objective optimization: present and future in de novo drug design. Front Chem 2023; 11:1288626. [PMID: 38192501 PMCID: PMC10773868 DOI: 10.3389/fchem.2023.1288626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024] Open
Abstract
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.
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Affiliation(s)
| | | | | | - Laurent E. Dardenne
- Coordenação de Modelagem Computacional, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
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25
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Mwangi J, Kamau PM, Thuku RC, Lai R. Design methods for antimicrobial peptides with improved performance. Zool Res 2023; 44:1095-1114. [PMID: 37914524 PMCID: PMC10802102 DOI: 10.24272/j.issn.2095-8137.2023.246] [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] [Received: 08/02/2023] [Accepted: 09/20/2023] [Indexed: 11/03/2023] Open
Abstract
The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult. In this regard, developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority. Antimicrobial peptides (AMPs), a ubiquitous class of naturally occurring compounds with broad-spectrum antipathogenic activity, hold significant promise as an effective solution to the current antimicrobial resistance (AMR) crisis. Several AMPs have been identified and evaluated for their therapeutic application, with many already in the drug development pipeline. Their distinct properties, such as high target specificity, potency, and ability to bypass microbial resistance mechanisms, make AMPs a promising alternative to traditional antibiotics. Nonetheless, several challenges, such as high toxicity, lability to proteolytic degradation, low stability, poor pharmacokinetics, and high production costs, continue to hamper their clinical applicability. Therefore, recent research has focused on optimizing the properties of AMPs to improve their performance. By understanding the physicochemical properties of AMPs that correspond to their activity, such as amphipathicity, hydrophobicity, structural conformation, amino acid distribution, and composition, researchers can design AMPs with desired and improved performance. In this review, we highlight some of the key strategies used to optimize the performance of AMPs, including rational design and de novo synthesis. We also discuss the growing role of predictive computational tools, utilizing artificial intelligence and machine learning, in the design and synthesis of highly efficacious lead drug candidates.
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Affiliation(s)
- James Mwangi
- Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Peptides of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Centre for Non-Human Primates, Kunming Primate Research Centre, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Sino-African Joint Research Centre, New Cornerstone Science Institute, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Peter Muiruri Kamau
- Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Peptides of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Centre for Non-Human Primates, Kunming Primate Research Centre, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Sino-African Joint Research Centre, New Cornerstone Science Institute, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Rebecca Caroline Thuku
- Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Peptides of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Centre for Non-Human Primates, Kunming Primate Research Centre, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Sino-African Joint Research Centre, New Cornerstone Science Institute, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Ren Lai
- Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Peptides of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Centre for Non-Human Primates, Kunming Primate Research Centre, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Sino-African Joint Research Centre, New Cornerstone Science Institute, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Centre for Evolution and Conservation Biology, Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, Guangdong 511458, China. E-mail:
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Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, Safi SZ, Singh SK, Velmurugan D, Selvaraj C. Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors. Methods 2023; 219:82-94. [PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010] [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] [Received: 08/07/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023] Open
Abstract
Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.
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Affiliation(s)
| | | | - Baskaralingam Vaseeharan
- Department of Animal Health and Management, Science Block, Alagappa University, Karaikudi, Tamil Nadu 630 003, India
| | - Abdulaziz S Alothaim
- Department of Biology, College of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
| | - Esam S Al-Malki
- Department of Biology, College of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
| | - Babu Snekaa
- Laboratory for Artificial Intelligence and Molecular Modelling, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu 600077, India
| | - Sher Zaman Safi
- Faculty of Medicine, Bioscience and Nursing, MAHSA University, Jenjarom 42610, Selangor, Malaysia
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Devadasan Velmurugan
- Department of Biotechnology, College of Engineering & Technology, SRM Institute of Science & Technology, Kattankulathur, Chennai, Tamil Nadu 603203, India
| | - Chandrabose Selvaraj
- Laboratory for Artificial Intelligence and Molecular Modelling, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu 600077, India; Laboratory for Artificial Intelligence and Molecular Modelling, Center for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu 602105, India.
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da Fonseca AM, Cabongo SQ, Caluaco BJ, Colares RP, Fernandes CFC, Dos Santos HS, de Lima-Neto P, Marinho ES. The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence. J Biomol Struct Dyn 2023; 41:9890-9906. [PMID: 36420665 DOI: 10.1080/07391102.2022.2148128] [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] [Received: 08/11/2022] [Accepted: 11/10/2022] [Indexed: 11/25/2022]
Abstract
The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of -17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of -10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of -7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Aluísio Marques da Fonseca
- Mestrado Acadêmico em Sociobiodiversidades e Tecnologias Sustentáveis - MASTS, Instituto de Engenharias e Desenvolvimento Sustentável, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Sadrack Queque Cabongo
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Bernardino Joaquim Caluaco
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Regilany Paulo Colares
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | | | | | - Pedro de Lima-Neto
- Department of Analytical Chemistry and Physical Chemistry, Science Center, Federal University of Ceara, Fortaleza, CE, Brazil
| | - Emmanuel Silva Marinho
- Grupo de química Teorica e Eletroquimica-GQTE, Universidade Estadual do Ceará, Limoeiro do Norte, CE, Brazil
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Liu N, Jin H, Zhang L, Liu Z. Plug-in Models: A Promising Direction for Molecular Generation. HEALTH DATA SCIENCE 2023; 3:0092. [PMID: 38487202 PMCID: PMC10880158 DOI: 10.34133/hds.0092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/26/2023] [Indexed: 03/17/2024]
Affiliation(s)
- Ningfeng Liu
- State Key Laboratory of Natural and Biomimetic Drugs,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
| | - Hongwei Jin
- State Key Laboratory of Natural and Biomimetic Drugs,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
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Haroon S, C A H, A S J. Generative Pre-trained Transformer (GPT) based model with relative attention for de novo drug design. Comput Biol Chem 2023; 106:107911. [PMID: 37450999 DOI: 10.1016/j.compbiolchem.2023.107911] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 06/24/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023]
Abstract
De novo drug design refers to the process of designing new drug molecules from scratch using computational methods. In contrast to other computational methods that primarily focus on modifying existing molecules, designing from scratch enables the exploration of new chemical space and the potential discovery of novel molecules with enhanced properties. In this research, we proposed a model that utilizes Generative Pre-trained Transformer (GPT) architecture and relative attention for de novo drug design. GPT is a language model that utilizes transformer architecture to predict the next word or token in a given sequence. Representation of molecules using SMILES notation has enabled the use of next-token prediction techniques in de novo drug design. GPT uses attention mechanisms to capture the dependencies and relationships between different tokens in a sequence and allows the model to focus on the most important information when processing the input. Relative attention is a variant of the attention mechanism, which allows the model to capture the relative distances and relationships between tokens in the input sequence. In the standard attention mechanism, positional information is typically encoded using fixed-position embeddings. In relative attention, positional information is supplied dynamically during attention calculation by incorporating relative positional encodings, enabling the model to quickly learn the syntax of new unseen tokens. Relative attention enables the GPT model to better understand the relative positions of tokens in the sequence, which can be particularly useful when dealing with limited dataset sizes or generating target-specific drugs. The proposed model was trained on benchmark datasets, and performance was compared with other generative models. We show that relative attention and transfer learning could enable the GPT model to generate molecules with improved validity, uniqueness, and novelty in the context of de novo drug design. To illustrate the effectiveness of relative attention, the model was trained using transfer learning on three target-specific datasets, and the performance was compared with standard attention.
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Affiliation(s)
- Suhail Haroon
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India.
| | - Hafsath C A
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India
| | - Jereesh A S
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India.
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Matos GDR, Pak S, Rizzo RC. Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit. J Chem Inf Model 2023; 63:5803-5822. [PMID: 37698425 PMCID: PMC10694857 DOI: 10.1021/acs.jcim.3c01031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.
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Affiliation(s)
- Guilherme Duarte Ramos Matos
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Instituto de Química, Universidade de Brasília, Brasília, Distrito Federal, 70910-900, Brazil
| | - Steven Pak
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, New York, 11794, USA
| | - Robert C. Rizzo
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Institute of Chemical Biology & Drug Discovery, Stony Brook University, Stony Brook, New York 11794, USA
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
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Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel) 2023; 16:1259. [PMID: 37765069 PMCID: PMC10537003 DOI: 10.3390/ph16091259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
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Affiliation(s)
| | | | | | | | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
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Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther 2023; 17:2691-2725. [PMID: 37701048 PMCID: PMC10493153 DOI: 10.2147/dddt.s424991] [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] [Received: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
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Alqahtani S. Improving on in-silico prediction of oral drug bioavailability. Expert Opin Drug Metab Toxicol 2023; 19:665-670. [PMID: 37728393 DOI: 10.1080/17425255.2023.2261366] [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] [Received: 07/09/2023] [Accepted: 09/18/2023] [Indexed: 09/21/2023]
Abstract
INTRODUCTION Although significant development has been made in high-throughput screening of oral drug absorption and oral bioavailability, in silico prediction continues to play an important role in prediction of oral bioavailability and assisting in the proper selection of potential drug candidates. AREAS COVERED This review describes the improvements and latest modeling methods and algorithms available for the prediction of this important parameter. We performed a PubMed database search with a focus on the literature published in the last 15 years. EXPERT OPINION A tremendous efforts have been done in the past several years to develop reliable prediction tools that can provide accurate prediction for oral bioavailability. Several studies demonstrated new methodologies and techniques to develop either web-based in silico predictive tools or integrated PBPK models to predict oral bioavailability for new molecules. Improvements in the databases and the computational power will enhance the in silico prediction accuracy and reliability. Finally, introducing artificial intelligence to the drug development process will help improve the prediction tools.
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Affiliation(s)
- Saeed Alqahtani
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
- Clinical Pharmacokinetics and Pharmacogenetics Unit, King Saud University Medical City, Riyadh, Saudi Arabia
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Mazuryk J, Klepacka K, Kutner W, Sharma PS. Glyphosate Separating and Sensing for Precision Agriculture and Environmental Protection in the Era of Smart Materials. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37384557 DOI: 10.1021/acs.est.3c01269] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
The present article critically and comprehensively reviews the most recent reports on smart sensors for determining glyphosate (GLP), an active agent of GLP-based herbicides (GBHs) traditionally used in agriculture over the past decades. Commercialized in 1974, GBHs have now reached 350 million hectares of crops in over 140 countries with an annual turnover of 11 billion USD worldwide. However, rolling exploitation of GLP and GBHs in the last decades has led to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide of farm and companies' workers. Intoxication with these herbicides dysregulates the microbiome-gut-brain axis, cholinergic neurotransmission, and endocrine system, causing paralytic ileus, hyperkalemia, oliguria, pulmonary edema, and cardiogenic shock. Precision agriculture, i.e., an (information technology)-enhanced approach to crop management, including a site-specific determination of agrochemicals, derives from the benefits of smart materials (SMs), data science, and nanosensors. Those typically feature fluorescent molecularly imprinted polymers or immunochemical aptamer artificial receptors integrated with electrochemical transducers. Fabricated as portable or wearable lab-on-chips, smartphones, and soft robotics and connected with SM-based devices that provide machine learning algorithms and online databases, they integrate, process, analyze, and interpret massive amounts of spatiotemporal data in a user-friendly and decision-making manner. Exploited for the ultrasensitive determination of toxins, including GLP, they will become practical tools in farmlands and point-of-care testing. Expectedly, smart sensors can be used for personalized diagnostics, real-time water, food, soil, and air quality monitoring, site-specific herbicide management, and crop control.
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Affiliation(s)
- Jarosław Mazuryk
- Department of Electrode Processes, Institute of Physical Chemistry, Polish Academy of Sciences, 01-224 Warsaw, Poland
- Bio & Soft Matter, Institute of Condensed Matter and Nanosciences, Université catholique de Louvain, 1 Place Louis Pasteur, 1348 Louvain-la-Neuve, Belgium
| | - Katarzyna Klepacka
- Functional Polymers Research Team, Institute of Physical Chemistry, Polish Academy of Sciences, 01-224 Warsaw, Poland
- ENSEMBLE3 sp. z o. o., 01-919 Warsaw, Poland
- Faculty of Mathematics and Natural Sciences. School of Sciences, Cardinal Stefan Wyszynski University in Warsaw, 01-938 Warsaw, Poland
| | - Włodzimierz Kutner
- Faculty of Mathematics and Natural Sciences. School of Sciences, Cardinal Stefan Wyszynski University in Warsaw, 01-938 Warsaw, Poland
- Modified Electrodes for Potential Application in Sensors and Cells Research Team, Institute of Physical Chemistry, Polish Academy of Sciences, 01-224 Warsaw, Poland
| | - Piyush Sindhu Sharma
- Functional Polymers Research Team, Institute of Physical Chemistry, Polish Academy of Sciences, 01-224 Warsaw, Poland
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Azad I, Khan T, Ahmad N, Khan AR, Akhter Y. Updates on drug designing approach through computational strategies: a review. Future Sci OA 2023; 9:FSO862. [PMID: 37180609 PMCID: PMC10167725 DOI: 10.2144/fsoa-2022-0085] [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: 12/14/2022] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.
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Affiliation(s)
- Iqbal Azad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Tahmeena Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Naseem Ahmad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Abdul Rahman Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, UP, 2260025, India
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Dost K, Pullar-Strecker Z, Brydon L, Zhang K, Hafner J, Riddle PJ, Wicker JS. Combatting over-specialization bias in growing chemical databases. J Cheminform 2023; 15:53. [PMID: 37208694 DOI: 10.1186/s13321-023-00716-w] [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: 10/04/2022] [Accepted: 03/25/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Predicting in advance the behavior of new chemical compounds can support the design process of new products by directing the research toward the most promising candidates and ruling out others. Such predictive models can be data-driven using Machine Learning or based on researchers' experience and depend on the collection of past results. In either case: models (or researchers) can only make reliable assumptions about compounds that are similar to what they have seen before. Therefore, consequent usage of these predictive models shapes the dataset and causes a continuous specialization shrinking the applicability domain of all trained models on this dataset in the future, and increasingly harming model-based exploration of the space. PROPOSED SOLUTION In this paper, we propose CANCELS (CounterActiNg Compound spEciaLization biaS), a technique that helps to break the dataset specialization spiral. Aiming for a smooth distribution of the compounds in the dataset, we identify areas in the space that fall short and suggest additional experiments that help bridge the gap. Thereby, we generally improve the dataset quality in an entirely unsupervised manner and create awareness of potential flaws in the data. CANCELS does not aim to cover the entire compound space and hence retains a desirable degree of specialization to a specified research domain. RESULTS An extensive set of experiments on the use-case of biodegradation pathway prediction not only reveals that the bias spiral can indeed be observed but also that CANCELS produces meaningful results. Additionally, we demonstrate that mitigating the observed bias is crucial as it cannot only intervene with the continuous specialization process, but also significantly improves a predictor's performance while reducing the number of required experiments. Overall, we believe that CANCELS can support researchers in their experimentation process to not only better understand their data and potential flaws, but also to grow the dataset in a sustainable way. All code is available under github.com/KatDost/Cancels .
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Affiliation(s)
- Katharina Dost
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand.
- enviPath UG & Co. KG, In den Graswiesen 13, 55437, Ockenheim, Germany.
| | - Zac Pullar-Strecker
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Liam Brydon
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Kunyang Zhang
- Eawag-Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland
| | - Jasmin Hafner
- Eawag-Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland
| | - Patricia J Riddle
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Jörg S Wicker
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
- enviPath UG & Co. KG, In den Graswiesen 13, 55437, Ockenheim, Germany
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37
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Kao PY, Yang YC, Chiang WY, Hsiao JY, Cao Y, Aliper A, Ren F, Aspuru-Guzik A, Zhavoronkov A, Hsieh MH, Lin YC. Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry. J Chem Inf Model 2023. [PMID: 37171372 DOI: 10.1021/acs.jcim.3c00562] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.
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Affiliation(s)
- Po-Yu Kao
- Insilico Medicine Taiwan Ltd., Taipei 110208, Taiwan
| | - Ya-Chu Yang
- Insilico Medicine Taiwan Ltd., Taipei 110208, Taiwan
| | - Wei-Yin Chiang
- Hon Hai (Foxconn) Research Institute, Taipei 114699, Taiwan
| | - Jen-Yueh Hsiao
- Hon Hai (Foxconn) Research Institute, Taipei 114699, Taiwan
| | - Yudong Cao
- Zapata Computing, Inc., Boston, Massachusetts 02110, United States
| | - Alex Aliper
- Insilico Medicine AI Limited, Masdar City, Abu Dhabi 145748, UAE
| | - Feng Ren
- Insilico Medicine Shanghai Ltd., Shanghai 201203, China
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research, Toronto, ON M5S 1M1, Canada
| | | | - Min-Hsiu Hsieh
- Hon Hai (Foxconn) Research Institute, Taipei 114699, Taiwan
| | - Yen-Chu Lin
- Insilico Medicine Taiwan Ltd., Taipei 110208, Taiwan
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
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38
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Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023; 28:molecules28093906. [PMID: 37175316 PMCID: PMC10180087 DOI: 10.3390/molecules28093906] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023] Open
Abstract
The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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39
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Koutroumpa NM, Papavasileiou KD, Papadiamantis AG, Melagraki G, Afantitis A. A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation. Int J Mol Sci 2023; 24:6573. [PMID: 37047543 PMCID: PMC10095548 DOI: 10.3390/ijms24076573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.
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Affiliation(s)
- Nikoletta-Maria Koutroumpa
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
| | - Konstantinos D. Papavasileiou
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
- Department of ChemoInformatics, NovaMechanics MIKE., 185 45 Piraeus, Greece
| | - Anastasios G. Papadiamantis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
| | - Georgia Melagraki
- Division of Physical Sciences & Applications, Hellenic Military Academy, 166 73 Vari, Greece
| | - Antreas Afantitis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
- Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
- Department of ChemoInformatics, NovaMechanics MIKE., 185 45 Piraeus, Greece
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40
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Saah SA, Sakyi PO, Adu-Poku D, Boadi NO, Djan G, Amponsah D, Devine RNOA, Ayittey K. Docking and Molecular Dynamics Identify Leads against 5 Alpha Reductase 2 for Benign Prostate Hyperplasia Treatment. J CHEM-NY 2023. [DOI: 10.1155/2023/8880213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023] Open
Abstract
Steroid 5 alpha-reductase 2 (5αR-2) is a membrane-embedded protein that together with other isoforms plays a key role in the metabolism of steroids. This enzyme catalyzes the reduction of testosterone to the more potent ligand, dihydrotestosterone (DHT) in the prostate. Androgens, testosterone, and DHT play important roles in prostate growth, development, and function. At the same time, both testosterone and DHT have been implicated in the pathogenesis of benign prostate hyperplasia (BPH). Inhibition of the DHT formation, therefore, provides a therapeutic strategy that offers the possibility of preventing, delaying, or treating BPH. Currently, two steroidal drugs that inhibit 5αR-2, dutasteride and finasteride, have been approved for clinical use. These two come at a high cost and also portray undesirable sexual side effects which necessitate the need to find new chemotherapeutic alternatives for the disease. Based on the aforementioned, finasteride and dutasteride were subjected to scaffold hopping, fragment-based de novo design, molecular docking, and molecular dynamics simulations employing databases like ChEMBL, DrugBank, PubChem, ChemSpider, and Zinc15 in the identification of potential hits targeting 5αR-2. Altogether, ten novel compounds targeting 5αR-2 were identified with binding energies lower or comparable to finasteride and dutasteride, the main inhibitors for this target. Molecular docking and molecular dynamics simulations studies identify amino acid residues Glu57, Phe219, Phe223, and Leu224 to be critical for ligand binding and complex stability. The physicochemical and pharmacological profiling suggests the potential of the hit compounds to be drug-like and orally active. Similarly, the quality parameter assessments revealed the hits possess LELP greater than 3 implying their promise as lead-like molecules. The compounds A5, A9, and A10 were, respectively, predicted to treat prostate disorders with Pa (0.188, 0.361, and 0.270) and Pi (0.176, 0.050, and 0.093), while A8 and A9 were found to be associated with BPH treatment with Pa (0.09 and 0.127) and Pi (0.077 and 0.033), respectively. Structural similarity searches via DrugBank identified the drugs faropenem, acemetacin, estradiol valerate, and yohimbine to be useful for BPH treatment suggesting the de novo designed ligands as potential chemotherapeutic agents for treating this disease.
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41
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Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 31:691-702. [PMID: 36923950 PMCID: PMC10009646 DOI: 10.1016/j.omtn.2023.02.019] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.
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Affiliation(s)
- Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xuesong Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Sanyin Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shilin Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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42
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Tysinger EP, Rai BK, Sinitskiy AV. Can We Quickly Learn to "Translate" Bioactive Molecules with Transformer Models? J Chem Inf Model 2023; 63:1734-1744. [PMID: 36914216 DOI: 10.1021/acs.jcim.2c01618] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Meaningful exploration of the chemical space of druglike molecules in drug design is a highly challenging task due to a combinatorial explosion of possible modifications of molecules. In this work, we address this problem with transformer models, a type of machine learning (ML) model originally developed for machine translation. By training transformer models on pairs of similar bioactive molecules from the public ChEMBL data set, we enable them to learn medicinal-chemistry-meaningful, context-dependent transformations of molecules, including those absent from the training set. By retrospective analysis on the performance of transformer models on ChEMBL subsets of ligands binding to COX2, DRD2, or HERG protein targets, we demonstrate that the models can generate structures identical or highly similar to most active ligands, despite the models having not seen any ligands active against the corresponding protein target during training. Our work demonstrates that human experts working on hit expansion in drug design can easily and quickly employ transformer models, originally developed to translate texts from one natural language to another, to "translate" from known molecules active against a given protein target to novel molecules active against the same target.
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Affiliation(s)
- Emma P Tysinger
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Brajesh K Rai
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Anton V Sinitskiy
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
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43
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Molecular Property Prediction by Combining LSTM and GAT. Biomolecules 2023; 13:biom13030503. [PMID: 36979438 PMCID: PMC10046625 DOI: 10.3390/biom13030503] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/10/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
Molecular property prediction is an important direction in computer-aided drug design. In this paper, to fully explore the information from SMILE stings and graph data of molecules, we combined the SALSTM and GAT methods in order to mine the feature information of molecules from sequences and graphs. The embedding atoms are obtained through SALSTM, firstly using SMILES strings, and they are combined with graph node features and fed into the GAT to extract the global molecular representation. At the same time, data augmentation is added to enlarge the training dataset and improve the performance of the model. Finally, to enhance the interpretability of the model, the attention layers of both models are fused together to highlight the key atoms. Comparison with other graph-based and sequence-based methods, for multiple datasets, shows that our method can achieve high prediction accuracy with good generalizability.
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44
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Fromer JC, Coley CW. Computer-aided multi-objective optimization in small molecule discovery. PATTERNS (NEW YORK, N.Y.) 2023; 4:100678. [PMID: 36873904 PMCID: PMC9982302 DOI: 10.1016/j.patter.2023.100678] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties. Multi-objective molecular design is commonly addressed by combining properties of interest into a single objective function using scalarization, which imposes assumptions about relative importance and uncovers little about the trade-offs between objectives. In contrast to scalarization, Pareto optimization does not require knowledge of relative importance and reveals the trade-offs between objectives. However, it introduces additional considerations in algorithm design. In this review, we describe pool-based and de novo generative approaches to multi-objective molecular discovery with a focus on Pareto optimization algorithms. We show how pool-based molecular discovery is a relatively direct extension of multi-objective Bayesian optimization and how the plethora of different generative models extend from single-objective to multi-objective optimization in similar ways using non-dominated sorting in the reward function (reinforcement learning) or to select molecules for retraining (distribution learning) or propagation (genetic algorithms). Finally, we discuss some remaining challenges and opportunities in the field, emphasizing the opportunity to adopt Bayesian optimization techniques into multi-objective de novo design.
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Affiliation(s)
- Jenna C Fromer
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Connor W Coley
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA.,Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
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45
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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46
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Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
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Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
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47
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Yeh KB, Parekh FK, Mombo I, Leimer J, Hewson R, Olinger G, Fair JM, Sun Y, Hay J. Climate change and infectious disease: A prologue on multidisciplinary cooperation and predictive analytics. Front Public Health 2023; 11:1018293. [PMID: 36741948 PMCID: PMC9895942 DOI: 10.3389/fpubh.2023.1018293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 01/02/2023] [Indexed: 01/22/2023] Open
Abstract
Climate change impacts global ecosystems at the interface of infectious disease agents and hosts and vectors for animals, humans, and plants. The climate is changing, and the impacts are complex, with multifaceted effects. In addition to connecting climate change and infectious diseases, we aim to draw attention to the challenges of working across multiple disciplines. Doing this requires concentrated efforts in a variety of areas to advance the technological state of the art and at the same time implement ideas and explain to the everyday citizen what is happening. The world's experience with COVID-19 has revealed many gaps in our past approaches to anticipating emerging infectious diseases. Most approaches to predicting outbreaks and identifying emerging microbes of major consequence have been with those causing high morbidity and mortality in humans and animals. These lagging indicators offer limited ability to prevent disease spillover and amplifications in new hosts. Leading indicators and novel approaches are more valuable and now feasible, with multidisciplinary approaches also within our grasp to provide links to disease predictions through holistic monitoring of micro and macro ecological changes. In this commentary, we describe niches for climate change and infectious diseases as well as overarching themes for the important role of collaborative team science, predictive analytics, and biosecurity. With a multidisciplinary cooperative "all call," we can enhance our ability to engage and resolve current and emerging problems.
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Affiliation(s)
| | | | - Illich Mombo
- CIRMF, Franceville, Gabon, Central African Republic
| | | | - Roger Hewson
- UK Health Security Agency, Salisbury, United Kingdom
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Jeanne M. Fair
- Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Yijun Sun
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
| | - John Hay
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
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48
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A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0. Processes (Basel) 2023. [DOI: 10.3390/pr11020330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.
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49
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Ogawa K, Sakamoto D, Hosoki R. Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications. Chem Pharm Bull (Tokyo) 2023; 71:486-494. [PMID: 37394596 DOI: 10.1248/cpb.c23-00039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.
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Affiliation(s)
- Keiko Ogawa
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University
| | - Daiki Sakamoto
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University
| | - Rumiko Hosoki
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University
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50
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Jiang Y, Rubin L, Zhou Z, Zhang H, Su Q, Hou ST, Lazarovici P, Zheng W. Pharmacological therapies and drug development targeting SARS-CoV-2 infection. Cytokine Growth Factor Rev 2022; 68:13-24. [PMID: 36266222 PMCID: PMC9558743 DOI: 10.1016/j.cytogfr.2022.10.003] [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: 07/27/2022] [Revised: 10/07/2022] [Accepted: 10/07/2022] [Indexed: 01/30/2023]
Abstract
The development of therapies for SARS-CoV-2 infection, based on virus biology and pathology, and of large- and small-scale randomized controlled trials, have brought forward several antiviral and immunomodulatory drugs targeting the disease severity. Casirivimab/Imdevimab monoclonal antibodies and convalescent plasma to prevent virus entry, Remdesivir, Molnupiravir, and Paxlovid nucleotide analogs to prevent viral replication, a variety of repurposed JAK-STAT signaling pathway inhibitors, corticosteroids, and recombinant agonists/antagonists of cytokine and interferons have been found to provide clinical benefits in terms of mortality and hospitalization. However, current treatment options face multiple clinical needs, and therefore, in this review, we provide an update on the challenges of the existing therapeutics and highlight drug development strategies for COVID-19 therapy, based on ongoing clinical trials, meta-analyses, and clinical case reports.
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Affiliation(s)
- Yizhou Jiang
- Centre of Reproduction, Development and Aging and Institute of Translation Medicine, Faculty of Health Sciences, University of Macau, Macau 999078, China,Brain Research Centre and Department of Biology, School of Life Science, Southern University of Science and Technology, 1088 Xueyuan Blvd, Nanshan District, Shenzhen, Guangdong Province 518055, China
| | - Limor Rubin
- Allergy and Clinical Immunology Unit, Department of Medicine, Hadassah-Hebrew University Medical Center, Jerusalem 9112001, Israel
| | - Zhiwei Zhou
- Centre of Reproduction, Development and Aging and Institute of Translation Medicine, Faculty of Health Sciences, University of Macau, Macau 999078, China
| | - Haibo Zhang
- Anesthesia, Critical Care Medicine and Physiology, St. Michael’s Hospital, University of Toronto, Ontario, Canada
| | - Qiaozhu Su
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, United Kingdom
| | - Sheng-Tao Hou
- Brain Research Centre and Department of Biology, School of Life Science, Southern University of Science and Technology, 1088 Xueyuan Blvd, Nanshan District, Shenzhen, Guangdong Province 518055, China,Correspondence to: Brain Research Centre and Department of Biology, Southern University of Science and Technology, 1088 Xueyuan Blvd, Nanshan District, Shenzhen, Guangdong Province 518055, China
| | - Philip Lazarovici
- Pharmacology, School of Pharmacy Institute for Drug Research, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112002, Israel
| | - Wenhua Zheng
- Centre of Reproduction, Development and Aging and Institute of Translation Medicine, Faculty of Health Sciences, University of Macau, Macau 999078, China,Correspondence to: Faculty of Health Sciences, University of Macau, Room 3057, Building E12, Avenida de Universidade, Taipa, Macau, China
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