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Saifi I, Bhat BA, Hamdani SS, Bhat UY, Lobato-Tapia CA, Mir MA, Dar TUH, Ganie SA. Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science. J Biomol Struct Dyn 2024; 42:6523-6541. [PMID: 37434311 DOI: 10.1080/07391102.2023.2234039] [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: 02/12/2023] [Accepted: 07/03/2023] [Indexed: 07/13/2023]
Abstract
In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ifra Saifi
- Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India
| | - Basharat Ahmad Bhat
- Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Syed Suhail Hamdani
- Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Umar Yousuf Bhat
- Department of Zoology, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | | | - Mushtaq Ahmad Mir
- Department of Clinical Laboratory Sciences, College of Applied Medical Science, King Khalid University, KSA, Saudi Arabia
| | - Tanvir Ul Hasan Dar
- Department of Biotechnology, School of Biosciences and Biotechnology, BGSB University, Rajouri, India
| | - Showkat Ahmad Ganie
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
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Zhao D, Zhang Y, Chen Y, Li B, Zhou W, Wang L. Highly Accurate and Explainable Predictions of Small-Molecule Antioxidants for Eight In Vitro Assays Simultaneously through an Alternating Multitask Learning Strategy. J Chem Inf Model 2024. [PMID: 38888465 DOI: 10.1021/acs.jcim.4c00748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Small molecule antioxidants can inhibit or retard oxidation reactions and protect against free radical damage to cells, thus playing a key role in food, cosmetics, pharmaceuticals, the environment, as well as materials. Experimentally driven antioxidant discovery is a major paradigm, and computationally assisted antioxidants are rarely reported. In this study, a functional-group-based alternating multitask self-supervised molecular representation learning method is proposed to simultaneously predict the antioxidant activities of small molecules for eight commonly used in vitro antioxidant assays. Extensive evaluation results reveal that compared with the baseline models, the multitask FG-BERT model achieves the best overall predictive performance, with the highest average F1, BA, ROC-AUC, and PRC-AUC values of 0.860, 0.880, 0.954, and 0.937 for the test sets, respectively. The Y-scrambling testing results further demonstrate that such a deep learning model was not constructed by accident and that it has reliable predictive capabilities. Additionally, the excellent interpretability of the multitask FG-BERT model makes it easy to identify key structural fragments/groups that contribute significantly to the antioxidant effect of a given molecule. Finally, an online antioxidant activity prediction platform called AOP (freely available at https://aop.idruglab.cn/) and its local version were developed based on the high-quality multitask FG-BERT model for experts and nonexperts in the field. We anticipate that it will contribute to the discovery of novel small-molecule antioxidants.
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Affiliation(s)
- Duancheng Zhao
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yanhong Zhang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yihao Chen
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Biaoshun Li
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Wenguang Zhou
- Central Laboratory of The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan 528200, China
| | - Ling Wang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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Nakayama Y, Morishita S, Doi H, Hirano T, Kaneko H. Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning. ACS OMEGA 2024; 9:18488-18494. [PMID: 38680296 PMCID: PMC11044161 DOI: 10.1021/acsomega.4c00655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/27/2024] [Accepted: 03/27/2024] [Indexed: 05/01/2024]
Abstract
Pesticides are widely used to improve crop productivity by eliminating weeds and pests. Conventional pesticide development involves synthesizing compounds, testing their activities, and studying their effects on the ecosystem. However, as pesticide discovery has an extremely low success rate, many compounds must be synthesized and tested. To overcome the high human, financial, and time costs of this process, machine learning is attracting increasing attention. In this study, we used machine learning for the molecular design of novel seed compounds for herbicides and insecticides. Classification models were constructed by using compounds that had been tested as herbicides and insecticides, and an inverse analysis of the constructed models was conducted. In the molecular design of herbicides, we proposed 186 new samples as herbicides using ensemble learning and a method for expressing explanatory variables that consider the relationships among eight weed species. For the molecular design of insecticides, we used undersampling and ensemble learning for the analysis of unbalanced data. Based on approximately 340,000 compounds, 12 potential insecticides were proposed, of which 2 exhibited actual activity when tested. These results demonstrate the potential of the developed machine-learning method for rapidly identifying novel herbicides and insecticides.
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Affiliation(s)
- Yuki Nakayama
- Department
of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
| | - Saki Morishita
- Hokko
Chemical Industry Co., Ltd., 2165, Toda, Atsugi-shi, Kanagawa 243-0023, Japan
| | - Hayato Doi
- Hokko
Chemical Industry Co., Ltd., 2165, Toda, Atsugi-shi, Kanagawa 243-0023, Japan
| | - Tatsuya Hirano
- Hokko
Chemical Industry Co., Ltd., 2165, Toda, Atsugi-shi, Kanagawa 243-0023, Japan
| | - Hiromasa Kaneko
- Department
of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
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Meewan I, Panmanee J, Petchyam N, Lertvilai P. HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES. Sci Rep 2024; 14:9262. [PMID: 38649402 PMCID: PMC11035669 DOI: 10.1038/s41598-024-59933-4] [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: 01/09/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.
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Affiliation(s)
- Ittipat Meewan
- Center for Advanced Therapeutics, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand.
| | - Jiraporn Panmanee
- Research Center for Neuroscience, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - Nopphon Petchyam
- Center for Advanced Therapeutics, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - Pichaya Lertvilai
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92037, USA
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Arora S, Chettri S, Percha V, Kumar D, Latwal M. Artifical intelligence: a virtual chemist for natural product drug discovery. J Biomol Struct Dyn 2024; 42:3826-3835. [PMID: 37232451 DOI: 10.1080/07391102.2023.2216295] [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: 01/16/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shefali Arora
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Sukanya Chettri
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Versha Percha
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Mamta Latwal
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
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Iwata H, Nakai T, Koyama T, Matsumoto S, Kojima R, Okuno Y. VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search. J Chem Inf Model 2023; 63:7392-7400. [PMID: 37993764 PMCID: PMC10716893 DOI: 10.1021/acs.jcim.3c01220] [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] [Received: 08/03/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/24/2023]
Abstract
Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular generative model combining a graph-based deep neural network and a reinforcement learning technique. We evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has considerable potential to revolutionize drug discovery, materials science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development.
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Affiliation(s)
- Hiroaki Iwata
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Taichi Nakai
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Takuto Koyama
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Shigeyuki Matsumoto
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Ryosuke Kojima
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Yasushi Okuno
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
- HPC-
and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, Kobe-shi, Hyogo 650-0047, Japan
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Tang SW, Helmeste DM, Leonard BE. COVID-19 as a polymorphic inflammatory spectrum of diseases: a review with focus on the brain. Acta Neuropsychiatr 2023; 35:248-269. [PMID: 36861428 DOI: 10.1017/neu.2023.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
There appear to be huge variations and aberrations in the reported data in COVID-19 2 years now into the pandemic. Conflicting data exist at almost every level and also in the reported epidemiological statistics across different regions. It is becoming clear that COVID-19 is a polymorphic inflammatory spectrum of diseases, and there is a wide range of inflammation-related pathology and symptoms in those infected with the virus. The host's inflammatory response to COVID-19 appears to be determined by genetics, age, immune status, health status and stage of disease. The interplay of these factors may decide the magnitude, duration, types of pathology, symptoms and prognosis in the spectrum of COVID-19 disorders, and whether neuropsychiatric disorders continue to be significant. Early and successful management of inflammation reduces morbidity and mortality in all stages of COVID-19.
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Affiliation(s)
- Siu Wa Tang
- Department of Psychiatry, University of California, Irvine, Irvine, CA, USA
- Institute of Brain Medicine, Hong Kong, China
| | - Daiga Maret Helmeste
- Department of Psychiatry, University of California, Irvine, Irvine, CA, USA
- Institute of Brain Medicine, Hong Kong, China
| | - Brian E Leonard
- Institute of Brain Medicine, Hong Kong, China
- Department of Pharmacology, National University of Ireland, Galway, Ireland
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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [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: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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