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Byun J, Tai J, Kim B, Kim J, Jung S, Lee J, Song YW, Shin J, Kim TH. Identification of Hit Compounds Using Artificial Intelligence for the Management of Allergic Diseases. Int J Mol Sci 2024; 25:2280. [PMID: 38396957 PMCID: PMC10889320 DOI: 10.3390/ijms25042280] [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: 01/05/2024] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024] Open
Abstract
This study aimed to identify and evaluate drug candidates targeting the kinase inhibitory region of suppressor of cytokine signaling (SOCS) 3 for the treatment of allergic rhinitis (AR). Utilizing an artificial intelligence (AI)-based new drug development platform, virtual screening was conducted to identify compounds inhibiting the SH2 domain binding of SOCS3. Luminescence assays assessed the ability of these compounds to restore JAK-2 activity diminished by SOCS3. Jurkat T and BEAS-2B cells were utilized to investigate changes in SOCS3 and STAT3 expression, along with STAT3 phosphorylation in response to the identified compounds. In an OVA-induced allergic rhinitis mouse model, we measured serum levels of total IgE and OVA-specific IgE, performed real-time PCR on nasal mucosa samples to quantify Th2 cytokines and IFN-γ expression, and conducted immunohistochemistry to analyze eosinophil levels. Screening identified 20 hit compounds with robust binding affinities. As the concentration of SOCS3 increased, a corresponding decrease in JAK2 activity was observed. Compounds 5 and 8 exhibited significant efficacy in restoring JAK2 activity without toxicity. Treatment with these compounds resulted in reduced SOCS3 expression and the reinstatement of STAT3 phosphorylation in Jurkat T and BEAS-2B cells. In the OVA-induced allergic rhinitis mouse model, compounds 5 and 8 effectively alleviated nasal symptoms and demonstrated lower levels of immune markers compared to the allergy group. This study underscores the promising nonclinical efficacy of compounds identified through the AI-based drug development platform. These findings introduce innovative strategies for the treatment of AR and highlight the potential therapeutic value of targeting SOCS3 in managing AR.
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Affiliation(s)
- Junhyoung Byun
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Junhu Tai
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Byoungjae Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Neuroscience Research Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Jaehyeong Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Semyung Jung
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Juhyun Lee
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Youn woo Song
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Jaemin Shin
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Tae Hoon Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
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Ganjavi C, Eppler MB, Pekcan A, Biedermann B, Abreu A, Collins GS, Gill IS, Cacciamani GE. Publishers' and journals' instructions to authors on use of generative artificial intelligence in academic and scientific publishing: bibliometric analysis. BMJ 2024; 384:e077192. [PMID: 38296328 PMCID: PMC10828852 DOI: 10.1136/bmj-2023-077192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/29/2023] [Indexed: 02/05/2024]
Abstract
OBJECTIVES To determine the extent and content of academic publishers' and scientific journals' guidance for authors on the use of generative artificial intelligence (GAI). DESIGN Cross sectional, bibliometric study. SETTING Websites of academic publishers and scientific journals, screened on 19-20 May 2023, with the search updated on 8-9 October 2023. PARTICIPANTS Top 100 largest academic publishers and top 100 highly ranked scientific journals, regardless of subject, language, or country of origin. Publishers were identified by the total number of journals in their portfolio, and journals were identified through the Scimago journal rank using the Hirsch index (H index) as an indicator of journal productivity and impact. MAIN OUTCOME MEASURES The primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals. RESULTS Among the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%) explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals. CONCLUSIONS Guidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed.
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Affiliation(s)
- Conner Ganjavi
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Michael B Eppler
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Asli Pekcan
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Brett Biedermann
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Andre Abreu
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Gary S Collins
- UK EQUATOR Centre, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Inderbir S Gill
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Giovanni E Cacciamani
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
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Yuan Y, Zhang Y, Meng X, Liu Z, Wang B, Miao R, Zhang R, Su W, Liu L. EDC-DTI: An end-to-end deep collaborative learning model based on multiple information for drug-target interactions prediction. J Mol Graph Model 2023; 122:108498. [PMID: 37126908 DOI: 10.1016/j.jmgm.2023.108498] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/10/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023]
Abstract
Innovations in drug-target interactions (DTIs) prediction accelerate the progression of drug development. The introduction of deep learning models has a dramatic impact on DTIs prediction, with a distinct influence on saving time and money in drug discovery. This study develops an end-to-end deep collaborative learning model for DTIs prediction, called EDC-DTI, to identify new targets for existing drugs based on multiple drug-target-related information including homogeneous information and heterogeneous information by the way of deep learning. Our end-to-end model is composed of a feature builder and a classifier. Feature builder consists of two collaborative feature construction algorithms that extract the molecular properties and the topology property of networks, and the classifier consists of a feature encoder and a feature decoder which are designed for feature integration and DTIs prediction, respectively. The feature encoder, mainly based on the improved graph attention network, incorporates heterogeneous information into drug features and target features separately. The feature decoder is composed of multiple neural networks for predictions. Compared with six popular baseline models, EDC-DTI achieves highest predictive performance in the case of low computational costs. Robustness tests demonstrate that EDC-DTI is able to maintain strong predictive performance on sparse datasets. As well, we use the model to predict the most likely targets to interact with Simvastatin (DB00641), Nifedipine (DB01115) and Afatinib (DB08916) as examples. Results show that most of the predictions can be confirmed by literature with clear evidence.
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Affiliation(s)
- Yongna Yuan
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China.
| | - Yuhao Zhang
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Xiangbo Meng
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Zhenyu Liu
- School of Cyberspace Security, Gansu University of Political Science and Law, Anning West Road, Lanzhou, 730070, Gansu, China
| | - Bohan Wang
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Ruidong Miao
- School of Life Science, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Ruisheng Zhang
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Wei Su
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Lei Liu
- Duzhe Publishing Group Co. Ltd., DuZhe Road, Lanzhou, 730000, Gansu, China
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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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Cuomo A, Boutis A, Colonese F, Nocerino D. High-rate breakthrough cancer pain and tumour characteristics - literature review and case series. Drugs Context 2023; 12:dic-2022-11-1. [PMID: 36926050 PMCID: PMC10012833 DOI: 10.7573/dic.2022-11-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 02/08/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer pain requires careful comprehensive patient evaluation and an appropriate and personalized clinical approach by a trained multidisciplinary team. The proper assessment of breakthrough cancer pain (BTcP) is part of an all-inclusive multidimensional evaluation of the patient. The aim of this narrative review is to explore the relationship between high-rate BTcP, which strongly impacts health- related quality of life and tumour characteristics, in the face of novel approaches that should provide guidance for future clinical practice. The presentation of short, emblematic clinical reports also promotes knowledge of BTcP, which, despite the availability of numerous therapeutic approaches, remains underdiagnosed and undertreated. This article is part of the Management of breakthrough cancer pain Special Issue: https://www.drugsincontext.com/special_issues/management-of-breakthrough-cancer-pain.
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Affiliation(s)
- Arturo Cuomo
- IRCCS Istituto Nazionale Tumori Fondazione G Pascale, Napoli, Italy
| | - Anastasios Boutis
- First Department of Clinical Oncology, Theagenio Hospital, Thessaloniki, Greece
| | - Francesca Colonese
- Department Medical Oncology-ASST-Monza Ospedale San Gerardo, Monza, Italy
| | - Davide Nocerino
- IRCCS Istituto Nazionale Tumori Fondazione G Pascale, Napoli, Italy
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Swain SS, Hussain T. Combined Bioinformatics and Combinatorial Chemistry Tools to Locate Drug-Able Anti-TB Phytochemicals: A Cost-Effective Platform for Natural Product-Based Drug Discovery. Chem Biodivers 2022; 19:e202200267. [PMID: 36307750 DOI: 10.1002/cbdv.202200267] [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: 03/27/2022] [Accepted: 09/30/2022] [Indexed: 11/12/2022]
Abstract
Based on extensive experimental studies, a huge number of phytochemicals showed potential activity against tuberculosis (TB) at a lower minimum inhibitory concentration (MIC) and fewer toxicity profiles. However, these promising drugs have not been able to convert from 'lead' to 'mainstream' due to inadequate drug-ability profiles. Thus, early drug-prospective analyses are required at the primary stage to accelerate natural-product-based drug discovery with limited resources and time. In the present study, we have selected seventy-three potential anti-TB phytochemicals (MIC value ≤10 μg/mL) and assessed the drug-ability profiles using bioinformatics and combinatorial chemistry tools, systematically. Primarily, the molecular docking study was done against two putative drug targets, catalase-peroxidase enzyme (katG) and RNA polymerase subunit-β (rpoB) of Mycobacterium tuberculosis (Mtb) using AutoDock 4.2 software. Further, assessed the drug-ability score from Molsoft, toxicity profiles from ProTox, pharmacokinetics from SwisADME, hierarchical cluster analysis (HCA) by ChemMine tools and frontier molecular orbitals (FMOs) with Avogadro and structural activity relationships (SAR) analysis with ChemDraw 18.0 software. Above analyses indicated that, lower MIC exhibited anti-TB phytochemicals, abietane, 12-demethylmulticaulin exhibited poor docking and drug-ability scores, while tiliacorinine, 2-nortiliacorinine showed higher binding energy and drug-ability profiles. Overall, tiliacorinine, 2-nortiliacorinine, 7α-acetoxy-6β-hydroxyroyleanone (AHR), (2S)-naringenin and isovachhalcone were found as the most active and drug-able anti-TB candidates from 73 candidates. Phytochemicals are always a vital source of mainstream drugs, but the MIC value of a phytochemical is not sufficient for it to be promoted. An ideal drug-ability profile is therefore essential for achieving clinical success, where advanced bioinformatics tools help to assess and analyse that profile. Additionally, several natural pharmacophores found in existing anti-TB drugs in SAR analyses also provide crucial information for developing potential anti-TB drug. As a conclusion, combined bioinformatics and combinatorial chemistry are the most effective strategies to locate potent-cum-drug-able candidates in the current drug-development module.
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Affiliation(s)
- Shasank S Swain
- Division of Microbiology and NCDs, ICMR-Regional Medical Research Center, Bhubaneswar, 751023, Odisha, India
| | - Tahziba Hussain
- Division of Microbiology and NCDs, ICMR-Regional Medical Research Center, Bhubaneswar, 751023, Odisha, India
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Schleinitz J, Langevin M, Smail Y, Wehnert B, Grimaud L, Vuilleumier R. Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C-O Couplings. J Am Chem Soc 2022; 144:14722-14730. [PMID: 35939717 DOI: 10.1021/jacs.2c05302] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Synthetic yield prediction using machine learning is intensively studied. Previous work has focused on two categories of data sets: high-throughput experimentation data, as an ideal case study, and data sets extracted from proprietary databases, which are known to have a strong reporting bias toward high yields. However, predicting yields using published reaction data remains elusive. To fill the gap, we built a data set on nickel-catalyzed cross-couplings extracted from organic reaction publications, including scope and optimization information. We demonstrate the importance of including optimization data as a source of failed experiments and emphasize how publication constraints shape the exploration of the chemical space by the synthetic community. While machine learning models still fail to perform out-of-sample predictions, this work shows that adding chemical knowledge enables fair predictions in a low-data regime. Eventually, we hope that this unique public database will foster further improvements of machine learning methods for reaction yield prediction in a more realistic context.
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Affiliation(s)
- Jules Schleinitz
- LBM, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Maxime Langevin
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France.,Molecular Design Sciences─Integrated Drug Discovery, Sanofi R&D, 94400 Vitry-Sur-Seine, France
| | - Yanis Smail
- UPMC, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Benjamin Wehnert
- UPMC, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Laurence Grimaud
- LBM, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Rodolphe Vuilleumier
- PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
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Martinelli DD. Generative machine learning for de novo drug discovery: A systematic review. Comput Biol Med 2022; 145:105403. [PMID: 35339849 DOI: 10.1016/j.compbiomed.2022.105403] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 02/08/2023]
Abstract
Recent research on artificial intelligence indicates that machine learning algorithms can auto-generate novel drug-like molecules. Generative models have revolutionized de novo drug discovery, rendering the explorative process more efficient. Several model frameworks and input formats have been proposed to enhance the performance of intelligent algorithms in generative molecular design. In this systematic literature review of experimental articles and reviews over the last five years, machine learning models, challenges associated with computational molecule design along with proposed solutions, and molecular encoding methods are discussed. A query-based search of the PubMed, ScienceDirect, Springer, Wiley Online Library, arXiv, MDPI, bioRxiv, and IEEE Xplore databases yielded 87 studies. Twelve additional studies were identified via citation searching. Of the articles in which machine learning was implemented, six prominent algorithms were identified: long short-term memory recurrent neural networks (LSTM-RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), adversarial autoencoders (AAEs), evolutionary algorithms, and gated recurrent unit (GRU-RNNs). Furthermore, eight central challenges were designated: homogeneity of generated molecular libraries, deficient synthesizability, limited assay data, model interpretability, incapacity for multi-property optimization, incomparability, restricted molecule size, and uncertainty in model evaluation. Molecules were encoded either as strings, which were occasionally augmented using randomization, as 2D graphs, or as 3D graphs. Statistical analysis and visualization are performed to illustrate how approaches to machine learning in de novo drug design have evolved over the past five years. Finally, future opportunities and reservations are discussed.
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Applications of machine learning in computer-aided drug discovery. QRB DISCOVERY 2022. [PMID: 37529294 PMCID: PMC10392679 DOI: 10.1017/qrd.2022.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Abstract
Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.
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Lagoutte-Renosi J, Allemand F, Ramseyer C, Yesylevskyy S, Davani S. Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives. Drug Discov Today 2021; 27:985-1007. [PMID: 34863931 DOI: 10.1016/j.drudis.2021.11.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/02/2021] [Accepted: 11/25/2021] [Indexed: 01/10/2023]
Abstract
Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.
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Affiliation(s)
- Jennifer Lagoutte-Renosi
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire de Pharmacologie Clinique et Toxicologie-CHU de Besançon, 25000 Besançon, France
| | - Florentin Allemand
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France
| | - Christophe Ramseyer
- Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France
| | - Semen Yesylevskyy
- Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France; Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine, Nauky Sve. 46, Kyiv, Ukraine; Receptor.ai inc, 16192 Coastal Highway, Lewes, DE, USA
| | - Siamak Davani
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire de Pharmacologie Clinique et Toxicologie-CHU de Besançon, 25000 Besançon, France.
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