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Abubakar ML, Kapoor N, Sharma A, Gambhir L, Jasuja ND, Sharma G. Artificial Intelligence in Drug Identification and Validation: A Scoping Review. Drug Res (Stuttg) 2024; 74:208-219. [PMID: 38830370 DOI: 10.1055/a-2306-8311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.
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Affiliation(s)
| | - Neha Kapoor
- School of Applied Sciences, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
| | - Asha Sharma
- Department of Zoology, Swargiya P. N. K. S. Govt. PG College, Dausa, Rajasthan, India
| | - Lokesh Gambhir
- School of Basic and Applied Sciences, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India
| | | | - Gaurav Sharma
- School of Applied Sciences, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
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Matúška J, Bucinsky L, Gall M, Pitoňák M, Štekláč M. SchNetPack Hyperparameter Optimization for a More Reliable Top Docking Scores Prediction. J Phys Chem B 2024; 128:4943-4951. [PMID: 38733335 DOI: 10.1021/acs.jpcb.4c00296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
Options to improve the extrapolation power of the neural network designed using the SchNetPack package with respect to top docking scores prediction are presented. It is shown that hyperparameter tuning of the atomistic model representation (in the schnetpack.representation) improves the prediction of the top scoring compounds, which have characteristically a low incidence in randomized data sets for training of machine learning models. The prediction robustness is evaluated according to the mean square error (MSE) and the entropy of the average loss landscape decrease. Admittedly, the improvement of the top scoring compounds' prediction accuracy comes with the penalty of worsening the overall prediction power. It is revealed that the most impactful hyperparameter is the cutoff (5 Å is reported as the optimal choice). Other parameters (e.g., number of radial basis functions, number of interaction layers of the neural network, feature vector size or its batch size) are found to not affect the prediction robustness of the top scoring compounds in any comparable way relative to the cutoff. The MSE of the best docking score prediction (below -13 kcal/mol) improves from ca. 3.5 to 0.9 kcal/mol, while the prediction of less potent compounds (-13 to -11 kcal/mol) shows a lesser improvement, i.e., a decrease of MSE from 1.6 to 1.3 kcal/mol. Additionally, oversampling and undersampling of the training set with respect to the top scoring compounds' abundance is presented. The results indicate that the cutoff choice performs better than over- or undersampling of the training set, with undersampling performing better than oversampling.
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Affiliation(s)
- Ján Matúška
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
| | - Lukas Bucinsky
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
| | - Marián Gall
- Institute of Information Engineering, Automation and Mathematics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
- National SuperComputing Center, Dúbravská cesta č. 9, SK-84104 Bratislava, Slovak Republic
| | - Michal Pitoňák
- National SuperComputing Center, Dúbravská cesta č. 9, SK-84104 Bratislava, Slovak Republic
- Department of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská dolina Ilkovičova 6, SK-84215 Bratislava, Slovak Republic
| | - Marek Štekláč
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
- Computing Centre, Centre of Operations of the Slovak Academy of Sciences, Dúbravská cesta č. 9, SK-84535 Bratislava, Slovak Republic
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Moingeon P, Garbay C, Dahan M, Fermont I, Benmakhlouf A, Gouyette A, Poitou P, Saint-Pierre A. [The revolution of AI in drug development]. Med Sci (Paris) 2024; 40:369-376. [PMID: 38651962 DOI: 10.1051/medsci/2024028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
Abstract
Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.
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Abou Hajal A, Al Meslamani AZ. Insights into artificial intelligence utilisation in drug discovery. J Med Econ 2024; 27:304-308. [PMID: 38385328 DOI: 10.1080/13696998.2024.2315864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 02/05/2024] [Indexed: 02/23/2024]
Affiliation(s)
- Abdallah Abou Hajal
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
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Li C, Ye G, Jiang Y, Wang Z, Yu H, Yang M. Artificial Intelligence in battling infectious diseases: A transformative role. J Med Virol 2024; 96:e29355. [PMID: 38179882 DOI: 10.1002/jmv.29355] [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: 10/16/2023] [Revised: 12/01/2023] [Accepted: 12/17/2023] [Indexed: 01/06/2024]
Abstract
It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.
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Affiliation(s)
- Chunhui Li
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Guoguo Ye
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, The Third People's Hospital of Shenzhen, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Yinghan Jiang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Zhiming Wang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Haiyang Yu
- Hangzhou Yalla Information Technology Service Co., Ltd., Hangzhou, People's Republic of China
| | - Minghui Yang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
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Blaudin de Thé FX, Baudier C, Andrade Pereira R, Lefebvre C, Moingeon P. Transforming drug discovery with a high-throughput AI-powered platform: A 5-year experience with Patrimony. Drug Discov Today 2023; 28:103772. [PMID: 37717933 DOI: 10.1016/j.drudis.2023.103772] [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: 07/26/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 09/19/2023]
Abstract
High-throughput computational platforms are being established to accelerate drug discovery. Servier launched the Patrimony platform to harness computational sciences and artificial intelligence (AI) to integrate massive multimodal data from internal and external sources. Patrimony has enabled researchers to prioritize therapeutic targets based on a deep understanding of the pathophysiology of immuno-inflammatory diseases. Herein, we share our experience regarding main challenges and critical success factors faced when industrializing the platform and broadening its applications to neurological diseases. We emphasize the importance of integrating such platforms in an end-to-end drug discovery process and engaging human experts early on to ensure a transforming impact.
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Rao M, McDuffie E, Sachs C. Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics. TOXICS 2023; 11:875. [PMID: 37888725 PMCID: PMC10611213 DOI: 10.3390/toxics11100875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/12/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023]
Abstract
The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.
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Affiliation(s)
- Mohan Rao
- Neurocrine Biosciences, Inc., Nonclinical Toxicology, San Diego, CA 92130, USA; (E.M.); (C.S.)
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