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Rodoplu Solovchuk D. Advances in AI-assisted biochip technology for biomedicine. Biomed Pharmacother 2024; 177:116997. [PMID: 38943990 DOI: 10.1016/j.biopha.2024.116997] [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: 04/24/2024] [Revised: 06/13/2024] [Accepted: 06/15/2024] [Indexed: 07/01/2024] Open
Abstract
The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.
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Affiliation(s)
- Didem Rodoplu Solovchuk
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan.
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2
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De Carlo A, Ronchi D, Piastra M, Tosca EM, Magni P. Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks. Pharmaceutics 2024; 16:776. [PMID: 38931898 PMCID: PMC11207804 DOI: 10.3390/pharmaceutics16060776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/08/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation of molecules directly derived from Simplified Molecular Input Line Entry System (SMILE) notation. Information is processed sequentially, from substructures to the whole molecule, employing a bottom-up approach. The developed GNN is tested and compared with existing approaches using six benchmark datasets and by encompassing regression (lipophilicity and aqueous solubility) and classification (CYP2C9, CYP2C19, CYP2D6 and CYP3A4 inhibition) tasks. Results show the effectiveness of our model, which bypasses the computationally expensive retrieval and selection of molecular descriptors. This approach provides a valuable tool for high-throughput screening, facilitating early assessment of ADMET properties and enhancing the likelihood of drug success in the development pipeline.
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Affiliation(s)
| | | | | | | | - Paolo Magni
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, 27100 Pavia, Italy; (A.D.C.); (D.R.); (M.P.); (E.M.T.)
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3
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Zahid H, Tayara H, Chong KT. Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties. Arch Toxicol 2024:10.1007/s00204-024-03756-9. [PMID: 38619593 DOI: 10.1007/s00204-024-03756-9] [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: 02/23/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively.
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Affiliation(s)
- Hamza Zahid
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
- Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea.
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4
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Chen Z, Zhang L, Zhang P, Guo H, Zhang R, Li L, Li X. Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition. J Chem Inf Model 2024; 64:2528-2538. [PMID: 37864562 DOI: 10.1021/acs.jcim.3c01396] [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: 10/23/2023]
Abstract
Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most drugs and mediate many adverse drug reactions. Therefore, it is necessary to estimate the chemical inhibition of Cytochrome P450 enzymes in drug discovery and the food industry. In the past few decades, many computational models have been reported, and some provided good performance. However, there are still several issues that should be resolved for these models, such as single isoform, models with unbalanced performance, lack of structural characteristics analysis, and poor availability. In the present study, the deep learning models based on python using the Keras framework and TensorFlow were developed for the chemical inhibition of each CYP isoform. These models were established based on a large data set containing 85715 compounds extracted from the PubChem bioassay database. On external validation, the models provided good AUC values with 0.97, 0.94, 0.94, 0.96, and 0.94 for CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, respectively. The models can be freely accessed on the Web server named CYPi-DNNpredictor (cypi.sapredictor.cn), and the codes for the model were made open source in the Supporting Information. In addition, we also analyzed the structural characteristics of chemicals with CYP450 inhibition and detected the structural alerts (SAs), which should be responsible for the inhibition. The SAs were also made available online, named CYPi-SAdetector (cypisa.sapredictor.cn). The models can be used as a powerful tool for the prediction of CYP450 inhibitors, and the SAs should provide useful information for the mechanisms of Cytochrome P450 inhibition.
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Affiliation(s)
- Zhaoyang Chen
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Le Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Ling Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
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5
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Jeong E, Hong H, Lee YA, Kim KS. Potential Rheumatoid Arthritis-Associated Interstitial Lung Disease Treatment and Computational Approach for Future Drug Development. Int J Mol Sci 2024; 25:2682. [PMID: 38473928 PMCID: PMC11154459 DOI: 10.3390/ijms25052682] [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: 01/22/2024] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by swelling in at least one joint. Owing to an overactive immune response, extra-articular manifestations are observed in certain cases, with interstitial lung disease (ILD) being the most common. Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is characterized by chronic inflammation of the interstitial space, which causes fibrosis and the scarring of lung tissue. Controlling inflammation and pulmonary fibrosis in RA-ILD is important because they are associated with high morbidity and mortality. Pirfenidone and nintedanib are specific drugs against idiopathic pulmonary fibrosis and showed efficacy against RA-ILD in several clinical trials. Immunosuppressants and disease-modifying antirheumatic drugs (DMARDs) with anti-fibrotic effects have also been used to treat RA-ILD. Immunosuppressants moderate the overexpression of cytokines and immune cells to reduce pulmonary damage and slow the progression of fibrosis. DMARDs with mild anti-fibrotic effects target specific fibrotic pathways to regulate fibrogenic cellular activity, extracellular matrix homeostasis, and oxidative stress levels. Therefore, specific medications are required to effectively treat RA-ILD. In this review, the commonly used RA-ILD treatments are discussed based on their molecular mechanisms and clinical trial results. In addition, a computational approach is proposed to develop specific drugs for RA-ILD.
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Affiliation(s)
- Eunji Jeong
- Department of Medicine, College of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea;
| | - Hyunseok Hong
- Yale College, Yale University, New Haven, CT 06520, USA;
- Department of Clinical Pharmacology and Therapeutics, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yeon-Ah Lee
- Division of Rheumatology, Department of Internal Medicine, Kyung Hee University Hospital, Seoul 02447, Republic of Korea;
| | - Kyoung-Soo Kim
- Department of Medicine, College of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea;
- Department of Clinical Pharmacology and Therapeutics, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
- East-West Bone & Joint Disease Research Institute, Kyung Hee University Hospital at Gangdong, Seoul 05278, Republic of Korea
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6
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Qi X, Zhao Y, Qi Z, Hou S, Chen J. Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges. Molecules 2024; 29:903. [PMID: 38398653 PMCID: PMC10892089 DOI: 10.3390/molecules29040903] [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: 01/15/2024] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.
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Affiliation(s)
- Xin Qi
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Yuanchun Zhao
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Zhuang Qi
- School of Software, Shandong University, Jinan 250101, China;
| | - Siyu Hou
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Jiajia Chen
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
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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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8
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Dudas B, Miteva MA. Computational and artificial intelligence-based approaches for drug metabolism and transport prediction. Trends Pharmacol Sci 2024; 45:39-55. [PMID: 38072723 DOI: 10.1016/j.tips.2023.11.001] [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] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 01/07/2024]
Abstract
Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.
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Affiliation(s)
- Balint Dudas
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France
| | - Maria A Miteva
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France.
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9
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Wang R, Liu Z, Gong J, Zhou Q, Guan X, Ge G. An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors. J Chem Inf Model 2023; 63:7699-7710. [PMID: 38055780 DOI: 10.1021/acs.jcim.3c01241] [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: 12/08/2023]
Abstract
Cytochrome P450 3A4 (CYP3A4), a prominent member of the P450 enzyme superfamily, plays a crucial role in metabolizing various xenobiotics, including over 50% of clinically significant drugs. Evaluating CYP3A4 inhibition before drug approval is essential to avoiding potentially harmful pharmacokinetic drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Despite the development of several CYP inhibitor prediction models, the primary approach for screening CYP inhibitors still relies on experimental methods. This might stem from the limitations of existing models, which only provide deterministic classification outcomes instead of precise inhibition intensity (e.g., IC50) and often suffer from inadequate prediction reliability. To address this challenge, we propose an uncertainty-guided regression model to accurately predict the IC50 values of anti-CYP3A4 activities. First, a comprehensive data set of CYP3A4 inhibitors was compiled, consisting of 27,045 compounds with classification labels, including 4395 compounds with explicit IC50 values. Second, by integrating the predictions of the classification model trained on a larger data set and introducing an evidential uncertainty method to rank prediction confidence, we obtained a high-precision and reliable regression model. Finally, we use the evidential uncertainty values as a trustworthy indicator to perform a virtual screening of an in-house compound set. The in vitro experiment results revealed that this new indicator significantly improved the hit ratio and reduced false positives among the top-ranked compounds. Specifically, among the top 20 compounds ranked with uncertainty, 15 compounds were identified as novel CYP3A4 inhibitors, and three of them exhibited activities less than 1 μM. In summary, our findings highlight the effectiveness of incorporating uncertainty in compound screening, providing a promising strategy for drug discovery and development.
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Affiliation(s)
- Ruixuan Wang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Zhikang Liu
- School of Mathematics and Statistics, Central South University, Changsha 410083, China
| | - Jiahao Gong
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Qingping Zhou
- School of Mathematics and Statistics, Central South University, Changsha 410083, China
| | - Xiaoqing Guan
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Guangbo Ge
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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Yuan Y, Shi C, Zhao H. Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products. ACS Synth Biol 2023; 12:2650-2662. [PMID: 37607352 PMCID: PMC10615616 DOI: 10.1021/acssynbio.3c00234] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.
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Affiliation(s)
- Yujie Yuan
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Chengyou Shi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Departments of Chemistry, Biochemistry, and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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11
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Li L, Lu Z, Liu G, Tang Y, Li W. Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates. Chem Res Toxicol 2023; 36:1332-1344. [PMID: 37437120 DOI: 10.1021/acs.chemrestox.3c00065] [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: 07/14/2023]
Abstract
Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolism of ∼7% of marketed drugs. The in vitro drug interaction studies guidance for industry issued by the FDA stipulates that drug sponsors need to evaluate whether the investigated drugs interact with the major drug-metabolizing P450s including CYP2B6. Therefore, there has been greater attention to the development of predictive models for CYP2B6 inhibitors and substrates. In this study, conventional machine learning and deep learning models were developed to predict CYP2B6 inhibitors and substrates. Our results showed that the best CYP2B6 inhibitor model yielded the AUC values of 0.95 and 0.75 with the 10-fold cross-validation and the test set, respectively, and the best CYP2B6 substrate model produced the AUC values of 0.93 and 0.90 with the 10-fold cross-validation and the test set, respectively. The generalization ability of the CYP2B6 inhibitor and substrate models was assessed by using the external validation sets. Several significant substructural fragments relevant to CYP2B6 inhibitors and substrates were detected via frequency substructure analysis and information gain. In addition, the applicability domain of the models was defined by employing a nonparametric method based on the probability density distribution. We anticipate that our results would be useful for the prediction of potential CYP2B6 inhibitors and substrates in the early stage of drug discovery.
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Affiliation(s)
- Longqiang Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhou Lu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Ouzounis S, Panagiotopoulos V, Bafiti V, Zoumpoulakis P, Cavouras D, Kalatzis I, Matsoukas MT, Katsila T. A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023. [PMID: 37406257 PMCID: PMC10357106 DOI: 10.1089/omi.2023.0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.
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Affiliation(s)
- Sotiris Ouzounis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
- Cloudpharm PC, Athens, Greece
| | - Vasilis Panagiotopoulos
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
- Cloudpharm PC, Athens, Greece
| | - Vivi Bafiti
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Panagiotis Zoumpoulakis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
- Department of Food Science and Technology, University of West Attica, Egaleo, Greece
| | - Dionisis Cavouras
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
| | - Ioannis Kalatzis
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
| | - Minos-Timotheos Matsoukas
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
- Cloudpharm PC, Athens, Greece
| | - Theodora Katsila
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
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13
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Tran TTV, Tayara H, Chong KT. Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives. Pharmaceutics 2023; 15:pharmaceutics15041260. [PMID: 37111744 PMCID: PMC10143484 DOI: 10.3390/pharmaceutics15041260] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University-Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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14
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Ai D, Cai H, Wei J, Zhao D, Chen Y, Wang L. DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction. Front Pharmacol 2023; 14:1099093. [PMID: 37101544 PMCID: PMC10123292 DOI: 10.3389/fphar.2023.1099093] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/31/2023] [Indexed: 04/28/2023] Open
Abstract
Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse drug-drug interactions, many of which are mediated by CYPs, are one of the important causes for the premature termination of drug development and drug withdrawal from the market. In this work, we reported in silicon classification models to predict the inhibitory activity of molecules against these five CYP isoforms using our recently developed FP-GNN deep learning method. The evaluation results showed that, to the best of our knowledge, the multi-task FP-GNN model achieved the best predictive performance with the highest average AUC (0.905), F1 (0.779), BA (0.819), and MCC (0.647) values for the test sets, even compared to advanced machine learning, deep learning, and existing models. Y-scrambling testing confirmed that the results of the multi-task FP-GNN model were not attributed to chance correlation. Furthermore, the interpretability of the multi-task FP-GNN model enables the discovery of critical structural fragments associated with CYPs inhibition. Finally, an online webserver called DEEPCYPs and its local version software were created based on the optimal multi-task FP-GNN model to detect whether compounds bear potential inhibitory activity against CYPs, thereby promoting the prediction of drug-drug interactions in clinical practice and could be used to rule out inappropriate compounds in the early stages of drug discovery and/or identify new CYPs inhibitors.
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15
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Sinha K, Ghosh J, Sil PC. Machine Learning in Drug Metabolism Study. Curr Drug Metab 2022; 23:CDM-EPUB-128463. [PMID: 36578255 DOI: 10.2174/1389200224666221227094144] [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: 06/28/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 12/30/2022]
Abstract
Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resource-demanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-to-activity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.
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Affiliation(s)
| | | | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata-700054
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16
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Gill J, Moullet M, Martinsson A, Miljković F, Williamson B, Arends RH, Pilla Reddy V. Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions. CPT Pharmacometrics Syst Pharmacol 2022; 12:122-134. [PMID: 36382697 PMCID: PMC9835131 DOI: 10.1002/psp4.12884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 10/17/2022] [Accepted: 10/24/2022] [Indexed: 11/17/2022] Open
Abstract
Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug-drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine-learning models have been developed that can classify drug-drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug-drug interaction, regression-based machine learning should be explored. Therefore, this study investigated the use of regression-based machine learning to predict changes in drug exposure caused by pharmacokinetic drug-drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug-drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression-based supervised machine-learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross-validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression-based machine-learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug-drug interaction risk assessment for new drug candidates.
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Affiliation(s)
- Jaidip Gill
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology & Safety Sciences, Biopharmaceuticals Research & Development, AstraZenecaCambridgeUK
| | - Marie Moullet
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology & Safety Sciences, Biopharmaceuticals Research & Development, AstraZenecaCambridgeUK
| | - Anton Martinsson
- Imaging and Data AnalyticsClinical Pharmacology & Safety Sciences, Research & Development, AstraZenecaGothenburgSweden
| | - Filip Miljković
- Imaging and Data AnalyticsClinical Pharmacology & Safety Sciences, Research & Development, AstraZenecaGothenburgSweden
| | - Beth Williamson
- Oncology Drug Metabolism and Pharmacokinetics, Research & Development, AstraZenecaCambridgeUK,Present address:
Drug Metabolism and Pharmacokinetics, Union Chimique Belge (UCB)SurreyUK
| | - Rosalinda H. Arends
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology & Safety Sciences, Biopharmaceuticals, Research & Development, AstraZenecaGaithersburgMarylandUSA,Present address:
Bioinformatics & Data ScienceExelixisAlamedaCAUSA
| | - Venkatesh Pilla Reddy
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology & Safety Sciences, Biopharmaceuticals Research & Development, AstraZenecaCambridgeUK
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17
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Mateev E, Balkanska-Mitkova T, Peikova L, Dimitrova M, Kondeva-Burdina M. In vitro and in silico inhibition performance of choline against CYP1A2, CYP2D6 and CYP3A4. BIOTECHNOL BIOTEC EQ 2022. [DOI: 10.1080/13102818.2022.2144452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Emilio Mateev
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Tony Balkanska-Mitkova
- Department of Pharmacology, Pharmacotherapy and Toxicology, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Lily Peikova
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Maria Dimitrova
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Magdalena Kondeva-Burdina
- Department of Pharmacology, Pharmacotherapy and Toxicology, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
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18
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Danel T, Wojtuch A, Podlewska S. Generation of new inhibitors of selected cytochrome P450 subtypes- In silico study. Comput Struct Biotechnol J 2022; 20:5639-5651. [PMID: 36284709 PMCID: PMC9582735 DOI: 10.1016/j.csbj.2022.10.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 11/16/2022] Open
Abstract
Physicochemical and pharmacokinetic compound profile has crucial impact on compound potency to become a future drug. Ligands with desired activity profile cannot be used for treatment if they are characterized by unfavourable physicochemical or ADMET properties. In the study, we consider metabolic stability and focus on selected subtypes of cytochrome P450 - proteins, which take part in the first phase of compound transformations in the organism. We develop a protocol for generation of new potential inhibitors of selected cytochrome isoforms. Its subsequent stages are composed of generation and assessment of new derivatives of known cytochrome inhibitors, docking and evaluation of the compound possible inhibition on the basis of the obtained ligand-protein complexes. Besides the library of new potential agents inhibiting particular cytochrome subtypes, we also prepare a graph neural network that predicts the change in activity for all modifications of the starting molecule. In addition, we perform a systematic statistical study on the influence of particular substitutions on the potential inhibition properties of generated compounds (both mono- and di-substitutions are considered), provide explanations of the inhibitory predictions and prepare an on-line visualization platform enabling manual inspection of the results. The developed methodology can greatly support the design of new cytochrome P450 inhibitors with the overarching goal of generation of new metabolically stable compounds. It enables instant evaluation of possible compound-cytochrome interactions and selection of ligands with the highest potential of possessing desired biological activity.
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Key Words
- CYP inhibitors
- CYP, cytochrome P450
- CYP450
- DL, deep learning
- DNNs, deep neural networks
- Docking
- Explainability
- GNN, graph neural network
- Graph neural networks
- ML, machine learning
- MSE, mean squared error
- Morgan FP, Morgan fingerprint
- New compounds generation
- On-line platform
- QSPR, quantitative structure-property relationship
- RF, random forest
- SRD, sum of ranking differences
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Affiliation(s)
- Tomasz Danel
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Agnieszka Wojtuch
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Department of Medicinal Chemistry, 31-343 Kraków, Smętna Street 12, Poland,Corresponding author.
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19
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Verdura S, Encinar JA, Fernández-Arroyo S, Joven J, Cuyàs E, Bosch-Barrera J, Menendez JA. Silibinin Suppresses the Hyperlipidemic Effects of the ALK-Tyrosine Kinase Inhibitor Lorlatinib in Hepatic Cells. Int J Mol Sci 2022; 23:ijms23179986. [PMID: 36077379 PMCID: PMC9456400 DOI: 10.3390/ijms23179986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/27/2022] [Accepted: 08/30/2022] [Indexed: 11/25/2022] Open
Abstract
The third-generation anaplastic lymphoma tyrosine kinase inhibitor (ALK-TKI) lorlatinib has a unique side effect profile that includes hypercholesteremia and hypertriglyceridemia in >80% of lung cancer patients. Here, we tested the hypothesis that lorlatinib might directly promote the accumulation of cholesterol and/or triglycerides in human hepatic cells. We investigated the capacity of the hepatoprotectant silibinin to modify the lipid-modifying activity of lorlatinib. To predict clinically relevant drug−drug interactions if silibinin were used to clinically manage lorlatinib-induced hyperlipidemic effects in hepatic cells, we also explored the capacity of silibinin to interact with and block CYP3A4 activity using in silico computational descriptions and in vitro biochemical assays. A semi-targeted ultrahigh pressure liquid chromatography accurate mass quadrupole time-of-flight mass spectrometry with electrospray ionization (UHPLC-ESI-QTOF-MS/MS)-based lipidomic approach revealed that short-term treatment of hepatic cells with lorlatinib promotes the accumulation of numerous molecular species of cholesteryl esters and triglycerides. Silibinin treatment significantly protected the steady-state lipidome of hepatocytes against the hyperlipidemic actions of lorlatinib. Lipid staining confirmed the ability of lorlatinib to promote neutral lipid overload in hepatocytes upon long-term exposure, which was prevented by co-treatment with silibinin. Computational analyses and cell-free biochemical assays predicted a weak to moderate inhibitory activity of clinically relevant concentrations of silibinin against CYP3A4 when compared with recommended (rosuvastatin) and non-recommended (simvastatin) statins for lorlatinib-associated dyslipidemia. The elevated plasma cholesterol and triglyceride levels in lorlatinib-treated lung cancer patients might involve primary alterations in the hepatic accumulation of lipid intermediates. Silibinin could be clinically explored to reduce the undesirable hyperlipidemic activity of lorlatinib in lung cancer patients.
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Affiliation(s)
- Sara Verdura
- Metabolism and Cancer Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology, 17007 Girona, Spain
- Girona Biomedical Research Institute (IDIBGI), Salt, 17190 Girona, Spain
| | - José Antonio Encinar
- Institute of Research, Development and Innovation in Biotechnology of Elche (IDiBE) and Molecular and Cell Biology Institute (IBMC), Miguel Hernández University (UMH), 03207 Elche, Spain
| | - Salvador Fernández-Arroyo
- Department of Medicine and Surgery, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica (URB-CRB), Hospital Universitari de Sant Joan, Institut d’Investigació Sanitaria Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Jorge Joven
- Department of Medicine and Surgery, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica (URB-CRB), Hospital Universitari de Sant Joan, Institut d’Investigació Sanitaria Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Elisabet Cuyàs
- Metabolism and Cancer Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology, 17007 Girona, Spain
- Girona Biomedical Research Institute (IDIBGI), Salt, 17190 Girona, Spain
- Correspondence: (E.C.); (J.A.M.)
| | - Joaquim Bosch-Barrera
- Girona Biomedical Research Institute (IDIBGI), Salt, 17190 Girona, Spain
- Medical Oncology, Catalan Institute of Oncology, 17007 Girona, Spain
- Department of Medical Sciences, Medical School, University of Girona, 17071 Girona, Spain
| | - Javier A. Menendez
- Metabolism and Cancer Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology, 17007 Girona, Spain
- Girona Biomedical Research Institute (IDIBGI), Salt, 17190 Girona, Spain
- Correspondence: (E.C.); (J.A.M.)
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20
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Wang J, Lou C, Liu G, Li W, Wu Z, Tang Y. Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening. Brief Bioinform 2022; 23:6673852. [PMID: 35998896 DOI: 10.1093/bib/bbac351] [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: 04/12/2022] [Revised: 07/13/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.
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Affiliation(s)
- Jiye Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Chaofeng Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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21
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Guttman Y, Kerem Z. Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI). Int J Mol Sci 2022; 23:ijms23158498. [PMID: 35955630 PMCID: PMC9369352 DOI: 10.3390/ijms23158498] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 07/26/2022] [Accepted: 07/28/2022] [Indexed: 02/01/2023] Open
Abstract
Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public.
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22
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Zhang S, Yan Z, Huang Y, Liu L, He D, Wang W, Fang X, Zhang X, Wang F, Wu H, Wang H. HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer. Bioinformatics 2022; 38:3444-3453. [PMID: 35604079 DOI: 10.1093/bioinformatics/btac342] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 05/06/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customised to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks, and self-supervised tasks. RESULTS Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements. AVAILABILITY H-ADMET is freely accessible at https://paddlehelix.baidu.com/app/drug/admet/train. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shanzhuo Zhang
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen, China
| | - Zhiyuan Yan
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen, China
| | - Yueyang Huang
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen, China
| | - Lihang Liu
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen, China
| | - Donglong He
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen, China
| | - Wei Wang
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Shenzhen, China
| | - Xiaomin Fang
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen, China
| | - Xiaonan Zhang
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen, China
| | - Fan Wang
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen, China
| | - Hua Wu
- Baidu Inc., Beijing, China
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23
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Moshawih S, Goh HP, Kifli N, Idris AC, Yassin H, Kotra V, Goh KW, Liew KB, Ming LC. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem Biol Drug Des 2022; 100:185-217. [PMID: 35490393 DOI: 10.1111/cbdd.14062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/15/2022] [Accepted: 04/23/2022] [Indexed: 11/28/2022]
Abstract
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.
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Affiliation(s)
- Said Moshawih
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hui Poh Goh
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Azam Che Idris
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hayati Yassin
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Vijay Kotra
- Faculty of Pharmacy, Quest International University, Perak, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Kai Bin Liew
- Faculty of Pharmacy, University of Cyberjaya, Cyberjaya, Malaysia
| | - Long Chiau Ming
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
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24
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Wang NN, Wang XG, Xiong GL, Yang ZY, Lu AP, Chen X, Liu S, Hou TJ, Cao DS. Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes. J Cheminform 2022; 14:23. [PMID: 35428354 PMCID: PMC9013037 DOI: 10.1186/s13321-022-00602-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/26/2022] [Indexed: 11/28/2022] Open
Abstract
Drug–drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in silico methods to predict and evaluate DDIs accurately and efficiently. In this study, based on a large number of substrates and inhibitors related to five important CYP450 isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4), a series of high-performance predictive models for metabolic DDIs were constructed by two machine learning methods (random forest and XGBoost) and 4 different types of descriptors (MOE_2D, CATS, ECFP4 and MACCS). To reduce the uncertainty of individual models, the consensus method was applied to yield more reliable predictions. A series of evaluations illustrated that the consensus models were more reliable and robust for the DDI predictions of new drug combination. For the internal validation, the whole prediction accuracy and AUC value of the DDI models were around 0.8 and 0.9, respectively. When it was applied to the external datasets, the model accuracy was 0.793 and 0.795 for multi-level validation and external validation, respectively. Furthermore, we also compared our model with some recently published tools and then applied the final model to predict FDA-approved drugs and proposed 54,013 possible drug pairs with potential DDIs. In summary, we developed a powerful DDI predictive model from the perspective of the CYP450 enzyme family and it will help a lot in the future drug development and clinical pharmacy research.
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25
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Sun X, Zhu J, Chen B, You H, Xu H. A feature transferring workflow between data-poor compounds in various tasks. PLoS One 2022; 17:e0266088. [PMID: 35353844 PMCID: PMC8967016 DOI: 10.1371/journal.pone.0266088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/14/2022] [Indexed: 12/03/2022] Open
Abstract
Compound screening by in silico approaches has advantages in identifying high-activity leading compounds and can predict the safety of the drug. A key challenge is that the number of observations of drug activity and toxicity accumulation varies by target in different datasets, some of which are more understudied than others. Owing to an overall insufficiency and imbalance of drug data, it is hard to accurately predict drug activity and toxicity of multiple tasks by the existing models. To solve this problem, this paper proposed a two-stage transfer learning workflow to develop a novel prediction model, which can accurately predict drug activity and toxicity of the targets with insufficient observations. We built a balanced dataset based on the Tox21 dataset and developed a drug activity and toxicity prediction model based on Siamese networks and graph convolution to produce multitasking output. We also took advantage of transfer learning from data-rich targets to data-poor targets. We showed greater accuracy in predicting the activity and toxicity of compounds to targets with rich data and poor data. In Tox21, a relatively rich dataset, the prediction model accuracy for classification tasks was 0.877 AUROC. In the other five unbalanced datasets, we also found that transfer learning strategies brought the accuracy of models to a higher level in understudied targets. Our models can overcome the imbalance in target data and predict the compound activity and toxicity of understudied targets to help prioritize upcoming biological experiments.
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Affiliation(s)
- Xiaofei Sun
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingyuan Zhu
- School of science, Harbin Institute of Technology, Shenzhen, Guangdong, China
| | - Bin Chen
- University of Chinese Academy of Sciences, Beijing, China
- IRIAI, Harbin Institute of Technology, Shenzhen, Guangdong, China
- * E-mail: (BC); (HY)
| | - Hengzhi You
- School of science, Harbin Institute of Technology, Shenzhen, Guangdong, China
- * E-mail: (BC); (HY)
| | - Huiqing Xu
- Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou, Guangdong, China
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26
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Guttman Y, Kerem Z. Dietary Inhibitors of CYP3A4 Are Revealed Using Virtual Screening by Using a New Deep-Learning Classifier. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:2752-2761. [PMID: 35104412 PMCID: PMC8895463 DOI: 10.1021/acs.jafc.2c00237] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 01/17/2022] [Accepted: 01/20/2022] [Indexed: 05/29/2023]
Abstract
CYP3A4 is the main human enzyme responsible for phase I metabolism of dietary compounds, prescribed drugs and xenobiotics, steroid hormones, and bile acids. The inhibition of CYP3A4 activity might impair physiological mechanisms, including the endocrine system and response to drug admission. Here, we aimed to discover new CYP3A4 inhibitors from food and dietary supplements. A deep-learning model was built that classifies compounds as either an inhibitor or noninhibitor, with a high specificity of 0.997. We used this classifier to virtually screen ∼60,000 dietary compounds. Of the 115 identified potential inhibitors, only 31 were previously suggested. Many herbals, as predicted here, might cause impaired metabolism of drugs, and endogenous hormones and bile acids. Additionally, by applying Lipinski's rules of five, 17 compounds were also classified as potential intestine local inhibitors. New CYP3A4 inhibitors predicted by the model, bilobetin and picropodophyllin, were assayed in vitro.
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27
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Dou H, Tan J, Wei H, Wang F, Yang J, Ma XG, Wang J, Zhou T. Transfer inhibitory potency prediction to binary classification: A model only needs a small training set. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106633. [PMID: 35091229 DOI: 10.1016/j.cmpb.2022.106633] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/28/2021] [Accepted: 01/10/2022] [Indexed: 06/14/2023]
Abstract
One of the most laborious for drug discovery is to select compounds from a library for experimental evaluation. Hence, we propose a machine learning model only needs to be trained on a small dataset to predict the inhibition constant (Ki) and half maximal inhibitory concentration (IC50) for a compound. We transfer the prediction task to a simpler binary classification task based on a naive but effective idea that we only need the related rank of a compound to determine whether to take it for further examination. To achieve this, we design a data augmentation strategy to effectively leverage the relationship between the compounds in the training set. After that, we formulate a new reward function for deep reinforcement learning to balance the feature selection and the accuracy. We employ a particle swarm optimized support vector machine for the binary classification task. Finally, a soft voting mechanism is introduced to solve the contradiction from the binary classification. Sufficient experiments show that our model achieves high and reliable accuracy, and is capable of ranking compounds based on a selected set of molecular descriptors. The current results show that our model provides a potential ligand-based in silico approach for prioritizing chemicals for experimental studies.
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Affiliation(s)
- Haowen Dou
- Department of Computer Science, Shantou University, Shantou, China
| | - Jie Tan
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, China
| | - Huiling Wei
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, China
| | - Fei Wang
- Department of Computer Science, Shantou University, Shantou, China; Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou, China
| | - Jinzhu Yang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - X-G Ma
- Foshan Graduate School, Northeastern University, Foshan, China; The State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China
| | - Jiaqi Wang
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, China.
| | - Teng Zhou
- Department of Computer Science, Shantou University, Shantou, China; Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou, China.
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28
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Zheng S, Wang L, Xiong J, Liang G, Xu Y, Lin F. Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods. J Chem Inf Model 2022; 62:1078-1099. [PMID: 35156807 DOI: 10.1021/acs.jcim.1c00948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.
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Affiliation(s)
- Suqing Zheng
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China.,Chemical Biology Research Center, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Lei Wang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Jun Xiong
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Guang Liang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China.,Chemical Biology Research Center, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Yong Xu
- Center of Chemical Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, P.R. China
| | - Fu Lin
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
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29
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Jiang D, Sun H, Wang J, Hsieh CY, Li Y, Wu Z, Cao D, Wu J, Hou T. Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning. Brief Bioinform 2022; 23:6513729. [DOI: 10.1093/bib/bbab597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/14/2021] [Accepted: 12/23/2021] [Indexed: 11/14/2022] Open
Abstract
Abstract
Accurate prediction of atomic partial charges with high-level quantum mechanics (QM) methods suffers from high computational cost. Numerous feature-engineered machine learning (ML)-based predictors with favorable computability and reliability have been developed as alternatives. However, extensive expertise effort was needed for feature engineering of atom chemical environment, which may consequently introduce domain bias. In this study, SuperAtomicCharge, a data-driven deep graph learning framework, was proposed to predict three important types of partial charges (i.e. RESP, DDEC4 and DDEC78) derived from high-level QM calculations based on the structures of molecules. SuperAtomicCharge was designed to simultaneously exploit the 2D and 3D structural information of molecules, which was proved to be an effective way to improve the prediction accuracy of the model. Moreover, a simple transfer learning strategy and a multitask learning strategy based on self-supervised descriptors were also employed to further improve the prediction accuracy of the proposed model. Compared with the latest baselines, including one GNN-based predictor and two ML-based predictors, SuperAtomicCharge showed better performance on all the three external test sets and had better usability and portability. Furthermore, the QM partial charges of new molecules predicted by SuperAtomicCharge can be efficiently used in drug design applications such as structure-based virtual screening, where the predicted RESP and DDEC4 charges of new molecules showed more robust scoring and screening power than the commonly used partial charges. Finally, two tools including an online server (http://cadd.zju.edu.cn/deepchargepredictor) and the source code command lines (https://github.com/zjujdj/SuperAtomicCharge) were developed for the easy access of the SuperAtomicCharge services.
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30
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He S, Zhao D, Ling Y, Cai H, Cai Y, Zhang J, Wang L. Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells. Front Pharmacol 2022; 12:796534. [PMID: 34975493 PMCID: PMC8719637 DOI: 10.3389/fphar.2021.796534] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 12/02/2021] [Indexed: 12/22/2022] Open
Abstract
Breast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naïve Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689–0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC (http://chembc.idruglab.cn/) and its local version software (https://github.com/idruglab/ChemBC) were developed to predict whether compounds have potential inhibitory activity against BC cells.
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Affiliation(s)
- Shuyun He
- 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, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Duancheng Zhao
- 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, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yanle Ling
- 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, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Hanxuan Cai
- 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, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yike Cai
- Center for Certification and Evaluation, Guangdong Drug Administration, Guangzhou, China
| | - Jiquan Zhang
- State Key Laboratory of Functions and Applications of Medicinal Plants, College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang, China
| | - Ling Wang
- 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, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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31
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Ryu JY, Lee JH, Lee BH, Song JS, Ahn S, Oh KS. PredMS: a random forest model for predicting metabolic stability of drug candidates in human liver microsomes. Bioinformatics 2022; 38:364-368. [PMID: 34515778 DOI: 10.1093/bioinformatics/btab547] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 07/22/2021] [Accepted: 09/08/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Poor metabolic stability leads to drug development failure. Therefore, it is essential to evaluate the metabolic stability of small compounds for successful drug discovery and development. However, evaluating metabolic stability in vitro and in vivo is expensive, time-consuming and laborious. In addition, only a few free software programs are available for metabolic stability data and prediction. Therefore, in this study, we aimed to develop a prediction model that predicts the metabolic stability of small compounds. RESULTS We developed a computational model, PredMS, which predicts the metabolic stability of small compounds as stable or unstable in human liver microsomes. PredMS is based on a random forest model using an in-house database of metabolic stability data of 1917 compounds. To validate the prediction performance of PredMS, we generated external test data of 61 compounds. PredMS achieved an accuracy of 0.74, Matthew's correlation coefficient of 0.48, sensitivity of 0.70, specificity of 0.86, positive predictive value of 0.94 and negative predictive value of 0.46 on the external test dataset. PredMS will be a useful tool to predict the metabolic stability of small compounds in the early stages of drug discovery and development. AVAILABILITY AND IMPLEMENTATION The source code for PredMS is available at https://bitbucket.org/krictai/predms, and the PredMS web server is available at https://predms.netlify.app. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jae Yong Ryu
- Department of Biotechnology, Duksung Women's University, Seoul 01369, Republic of Korea
| | - Jeong Hyun Lee
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 34114 Daejeon, Republic of Korea
| | - Byung Ho Lee
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 34114 Daejeon, Republic of Korea
| | - Jin Sook Song
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 34114 Daejeon, Republic of Korea
| | - Sunjoo Ahn
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 34114 Daejeon, Republic of Korea.,Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, Daejeon 34129, Republic of Korea
| | - Kwang-Seok Oh
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 34114 Daejeon, Republic of Korea.,Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, Daejeon 34129, Republic of Korea
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32
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Goldwaser E, Laurent C, Lagarde N, Fabrega S, Nay L, Villoutreix BO, Jelsch C, Nicot AB, Loriot MA, Miteva MA. Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9. PLoS Comput Biol 2022; 18:e1009820. [PMID: 35081108 PMCID: PMC8820617 DOI: 10.1371/journal.pcbi.1009820] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/07/2022] [Accepted: 01/10/2022] [Indexed: 11/19/2022] Open
Abstract
Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.
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Affiliation(s)
- Elodie Goldwaser
- INSERM U1268 « Medicinal Chemistry and Translational Research », UMR 8038 CiTCoM, CNRS—University of Paris, Paris, France
| | | | - Nathalie Lagarde
- Laboratoire GBCM, EA7528, Conservatoire National des Arts et Métiers, 2 Rue Conté, Hésam Université, Paris, France
| | - Sylvie Fabrega
- Viral Vector for Gene Transfer core facility, Université de Paris—Structure Fédérative de Recherche Necker, INSERM US24/CNRS UMS3633, Paris, France
| | - Laure Nay
- Viral Vector for Gene Transfer core facility, Université de Paris—Structure Fédérative de Recherche Necker, INSERM US24/CNRS UMS3633, Paris, France
| | | | | | - Arnaud B. Nicot
- INSERM, Nantes Université, Center for Research in Transplantation and Translational Immunology, UMR 1064, ITUN, Nantes, France
| | - Marie-Anne Loriot
- University of Paris, INSERM U1138, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Biochimie, Paris, France
| | - Maria A. Miteva
- INSERM U1268 « Medicinal Chemistry and Translational Research », UMR 8038 CiTCoM, CNRS—University of Paris, Paris, France
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33
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Chand RR, Nimick M, Cridge B, Rosengren RJ. In Vitro Hepatic Assessment of Cineole and Its Derivatives in Common Brushtail Possums ( Trichosurus vulpecula) and Rodents. BIOLOGY 2021; 10:1326. [PMID: 34943241 PMCID: PMC8698377 DOI: 10.3390/biology10121326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 12/04/2022]
Abstract
Folivore marsupials, such as brushtail possum (Trichosurus Vulpecula) and koala (Phascolarctos cinereus), can metabolise higher levels of dietary terpenes, such as cineole, that are toxic to eutherian mammals. While the highly efficient drug metabolising enzymes, cytochrome P450 3A (CYP3A) and phase II conjugating enzymes (UDP-glucuronosyltransferase, UGT), are involved in the metabolism of high levels of dietary terpenes, evidence for inhibitory actions on these enzymes by these terpenes is scant. Thus, this study investigated the effect of cineole and its derivatives on catalytic activities of hepatic CYP3A and UGT in mice, rats, and possums. Results showed that cineole (up to 50 µM) and its derivatives (up to 25 µM) did not significantly inhibit CYP3A and UGT activities in mice, rats, and possums (both in silico and in vitro). Interestingly, basal hepatic CYP3A catalytic activity in the possums was ~20% lower than that in rats and mice. In contrast, possums had ~2-fold higher UGT catalytic activity when compared to mice and rats. Thus, these basal enzymatic differences may be further exploited in future pest management strategies.
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Affiliation(s)
- Ravneel R. Chand
- Department of Pharmacology and Toxicology, School of Biomedical Sciences, University of Otago, Dunedin 9016, New Zealand;
| | - Mhairi Nimick
- Department of Pharmacology and Toxicology, School of Biomedical Sciences, University of Otago, Dunedin 9016, New Zealand;
| | - Belinda Cridge
- Science for Communities, Christchurch Science Centre, Christchurch 8041, New Zealand;
| | - Rhonda J. Rosengren
- Department of Pharmacology and Toxicology, School of Biomedical Sciences, University of Otago, Dunedin 9016, New Zealand;
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34
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Park H, Brahma R, Shin J, Cho K. Prediction of human cytochrome
P450
inhibition using bio‐selectivity induced deep neural network. B KOREAN CHEM SOC 2021. [DOI: 10.1002/bkcs.12445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Hyejin Park
- AzothBio, Rm. DA724 Hyundai Knowledge Industry Center Hanam‐si Gyeonggi‐do Republic of Korea
| | - Rahul Brahma
- School of Systems Biomedical Science Soongsil University Seoul Republic of Korea
| | - Jae‐Min Shin
- AzothBio, Rm. DA724 Hyundai Knowledge Industry Center Hanam‐si Gyeonggi‐do Republic of Korea
| | - Kwang‐Hwi Cho
- School of Systems Biomedical Science Soongsil University Seoul Republic of Korea
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35
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Grebner C, Matter H, Hessler G. Artificial Intelligence in Compound Design. Methods Mol Biol 2021; 2390:349-382. [PMID: 34731477 DOI: 10.1007/978-1-0716-1787-8_15] [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: 02/10/2023]
Abstract
Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.
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Affiliation(s)
- Christoph Grebner
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Hans Matter
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Gerhard Hessler
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany.
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Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Mol Divers 2021; 26:1893-1913. [PMID: 34686947 PMCID: PMC8536481 DOI: 10.1007/s11030-021-10326-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/24/2021] [Indexed: 12/27/2022]
Abstract
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.
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Affiliation(s)
- Chandrabose Selvaraj
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
| | - Ishwar Chandra
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Sanjeev Kumar Singh
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
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Nguyen-Vo TH, Trinh QH, Nguyen L, Nguyen-Hoang PU, Nguyen TN, Nguyen DT, Nguyen BP, Le L. iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding. J Chem Inf Model 2021; 62:5059-5068. [PMID: 34672553 DOI: 10.1021/acs.jcim.1c00628] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The human cytochrome P450 (CYP) superfamily holds responsibilities for the metabolism of both endogenous and exogenous compounds such as drugs, cellular metabolites, and toxins. The inhibition exerted on the CYP enzymes is closely associated with adverse drug reactions encompassing metabolic failures and induced side effects. In modern drug discovery, identification of potential CYP inhibitors is, therefore, highly essential. Alongside experimental approaches, numerous computational models have been proposed to address this biochemical issue. In this study, we introduce iCYP-MFE, a computational framework for virtual screening on CYP inhibitors toward 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms. iCYP-MFE contains a set of five robust, stable, and effective prediction models developed using multitask learning incorporated with molecular fingerprint-embedded features. The results show that multitask learning can remarkably leverage useful information from related tasks to promote global performance. Comparative analysis indicates that iCYP-MFE achieves three predominant tasks, one equivalent task, and one less effective task compared to state-of-the-art methods. The area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR) were two decisive metrics used for model evaluation. The prediction task for CYP2D6-inhibition achieves the highest AUC-ROC value of 0.93 while the prediction task for CYP1A2-inhibition obtains the highest AUC-PR value of 0.92. The substructural analysis preliminarily explains the nature of the CYP-inhibitory activity of compounds. An online web server for iCYP-MFE with a user-friendly interface was also deployed to support scientific communities in identifying CYP inhibitors.
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Affiliation(s)
- Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6140, New Zealand
| | - Quang H Trinh
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Loc Nguyen
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Phuong-Uyen Nguyen-Hoang
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Thien-Ngan Nguyen
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Dung T Nguyen
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
| | - Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6140, New Zealand
| | - Ly Le
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam.,Vingroup Big Data Institute, Ha Noi 100000, Vietnam
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38
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Plonka W, Stork C, Šícho M, Kirchmair J. CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes. Bioorg Med Chem 2021; 46:116388. [PMID: 34488021 DOI: 10.1016/j.bmc.2021.116388] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/19/2021] [Accepted: 08/24/2021] [Indexed: 10/20/2022]
Abstract
The vast majority of approved drugs are metabolized by the five major cytochrome P450 (CYP) isozymes, 1A2, 2C9, 2C19, 2D6 and 3A4. Inhibition of CYP isozymes can cause drug-drug interactions with severe pharmacological and toxicological consequences. Computational methods for the fast and reliable prediction of the inhibition of CYP isozymes by small molecules are therefore of high interest and relevance to pharmaceutical companies and a host of other industries, including the cosmetics and agrochemical industries. Today, a large number of machine learning models for predicting the inhibition of the major CYP isozymes by small molecules are available. With this work we aim to go beyond the coverage of existing models, by combining data from several major public and proprietary sources. More specifically, we used up to 18815 compounds with measured bioactivities to train random forest classification models for the individual CYP isozymes. A major advantage of the new data collection over existing ones is the better representation of the minority class, the CYP inhibitors. With the new data collection we achieved inhibitor-to-non-inhibitor ratios in the order of 1:1 (CYP1A2) to 1:3 (CYP2D6). We show that our models reach competitive performance on external data, with Matthews correlation coefficients (MCCs) ranging from 0.62 (CYP2C19) to 0.70 (CYP2D6), and areas under the receiver operating characteristic curve (AUCs) between 0.89 (CYP2C19) and 0.92 (CYPs 2D6 and 3A4). Importantly, the models show a high level of robustness, reflected in a good predictivity also for compounds that are structurally dissimilar to the compounds represented in the training data. The best models presented in this work are freely accessible for academic research via a web service.
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Affiliation(s)
- Wojciech Plonka
- Universität Hamburg, Center for Bioinformatics (ZBH), Hamburg, Bundesstr. 43, 20146, Germany; FQS Poland (Fujitsu Group), Parkowa 11, 30-538 Cracow, Poland
| | - Conrad Stork
- Universität Hamburg, Center for Bioinformatics (ZBH), Hamburg, Bundesstr. 43, 20146, Germany
| | - Martin Šícho
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - Johannes Kirchmair
- Universität Hamburg, Center for Bioinformatics (ZBH), Hamburg, Bundesstr. 43, 20146, Germany; Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstr. 14, 1090 Vienna, Austria.
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Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
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Rácz A, Bajusz D, Miranda-Quintana RA, Héberger K. Machine learning models for classification tasks related to drug safety. Mol Divers 2021; 25:1409-1424. [PMID: 34110577 PMCID: PMC8342376 DOI: 10.1007/s11030-021-10239-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/27/2021] [Indexed: 12/23/2022]
Abstract
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary
| | | | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
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Zhai N, Wang C, Wu F, Xiong L, Luo X, Ju X, Liu G. Exploration of Novel Xanthine Oxidase Inhibitors Based on 1,6-Dihydropyrimidine-5-Carboxylic Acids by an Integrated in Silico Study. Int J Mol Sci 2021; 22:8122. [PMID: 34360886 PMCID: PMC8348919 DOI: 10.3390/ijms22158122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 07/19/2021] [Accepted: 07/22/2021] [Indexed: 02/07/2023] Open
Abstract
Xanthine oxidase (XO) is an important target for the effective treatment of hyperuricemia-associated diseases. A series of novel 2-substituted 6-oxo-1,6-dihydropyrimidine-5-carboxylic acids (ODCs) as XO inhibitors (XOIs) with remarkable activities have been reported recently. To better understand the key pharmacological characteristics of these XOIs and explore more hit compounds, in the present study, the three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) studies were performed on 46 ODCs. The constructed 3D-QSAR models exhibited reliable predictability with satisfactory validation parameters, including q2 = 0.897, R2 = 0.983, rpred2 = 0.948 in a CoMFA model, and q2 = 0.922, R2 = 0.990, rpred2 = 0.840 in a CoMSIA model. Docking and MD simulations further gave insights into the binding modes of these ODCs with the XO protein. The results indicated that key residues Glu802, Arg880, Asn768, Thr1010, Phe914, and Phe1009 could interact with ODCs by hydrogen bonds, π-π stackings, or hydrophobic interactions, which might be significant for the activity of these XOIs. Four potential hits were virtually screened out using the constructed pharmacophore model in combination with molecular dockings and ADME predictions. The four hits were also found to be relatively stable in the binding pocket by MD simulations. The results in this study might provide effective information for the design and development of novel XOIs.
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Affiliation(s)
- Na Zhai
- Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; (N.Z.); (C.W.); (F.W.); (X.L.); (X.J.)
| | - Chenchen Wang
- Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; (N.Z.); (C.W.); (F.W.); (X.L.); (X.J.)
| | - Fengshou Wu
- Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; (N.Z.); (C.W.); (F.W.); (X.L.); (X.J.)
| | - Liwei Xiong
- Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; (N.Z.); (C.W.); (F.W.); (X.L.); (X.J.)
- Hubei Key Laboratory of Plasma Chemistry and Advanced Materials, Wuhan Institute of Technology, Wuhan 430205, China
| | - Xiaogang Luo
- Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; (N.Z.); (C.W.); (F.W.); (X.L.); (X.J.)
- School of Materials Science and Engineering, Zhengzhou University, No. 100 Science Avenue, Zhengzhou 450001, China
| | - Xiulian Ju
- Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; (N.Z.); (C.W.); (F.W.); (X.L.); (X.J.)
| | - Genyan Liu
- Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; (N.Z.); (C.W.); (F.W.); (X.L.); (X.J.)
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42
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Zhang X, Zhao P, Wang Z, Xu X, Liu G, Tang Y, Li W. In Silico Prediction of CYP2C8 Inhibition with Machine-Learning Methods. Chem Res Toxicol 2021; 34:1850-1859. [PMID: 34255486 DOI: 10.1021/acs.chemrestox.1c00078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Cytochrome P450 2C8 (CYP2C8) is a major drug-metabolizing enzyme in humans and is responsible for the metabolism of ∼5% drugs in clinical use. Thus, inhibition of CYP2C8, which causes potential adverse drug events, cannot be neglected. The in vitro drug interaction studies guidelines for industry issued by the FDA also point out that it needs to be determined whether investigated drugs are CYP2C8 inhibitors before clinical trials. However, current studies mainly focus on predicting the inhibitors of other major P450 enzymes, and the importance of CYP2C8 inhibition has been overlooked. Therefore, there is a need to develop models for identifying potential CYP2C8 inhibition. In this study, in silico classification models for predicting CYP2C8 inhibition were built by five machine-learning methods combined with nine molecular fingerprints. The performance of the models built was evaluated by test and external validation sets. The best model had AUC values of 0.85 and 0.90 for the test and external validation sets, respectively. The applicability domain was analyzed based on the molecular similarity and exhibited an impact on the improvement of prediction accuracy. Furthermore, several representative privileged substructures such as 1H-benzo[d]imidazole, 1-phenyl-1H-pyrazole, and quinoline were identified by information gain and substructure frequency analysis. Overall, our results would be helpful for the prediction of CYP2C8 inhibition.
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Affiliation(s)
- Xiaoxiao Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Piaopiao Zhao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zhiyuan Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Xuan Xu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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Chen X, Chen Z, Xu D, Lyu Y, Li Y, Li S, Wang J, Wang Z. De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis. Front Bioeng Biotechnol 2021; 9:694100. [PMID: 34195182 PMCID: PMC8236607 DOI: 10.3389/fbioe.2021.694100] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 05/07/2021] [Indexed: 12/03/2022] Open
Abstract
G protein-coupled receptor 40 (GPR40), one of the G protein-coupled receptors that are available to sense glucose metabolism, is an attractive target for the treatment of type 2 diabetes mellitus (T2DM). Despite many efforts having been made to discover small-molecule agonists, there is limited research focus on developing peptides acting as GPR40 agonists to treat T2DM. Here, we propose a novel strategy for peptide design to generate and determine potential peptide agonists against GPR40 efficiently. A molecular fingerprint similarity (MFS) model combined with a deep neural network (DNN) and convolutional neural network was applied to predict the activity of peptides constructed by unnatural amino acids (UAAs). Site-directed mutagenesis (SDM) further optimized the peptides to form specific favorable interactions, and subsequent flexible docking showed the details of the binding mechanism between peptides and GPR40. Molecular dynamics (MD) simulations further verified the stability of the peptide–protein complex. The R-square of the machine learning model on the training set and the test set reached 0.87 and 0.75, respectively; and the three candidate peptides showed excellent performance. The strategy based on machine learning and SDM successfully searched for an optimal design with desirable activity comparable with the model agonist in phase III clinical trials.
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Affiliation(s)
- Xu Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.,School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.,School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Daiyun Xu
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Yonghui Lyu
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Yongxiao Li
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Shengbin Li
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Junqing Wang
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review. Mol Divers 2021; 25:1643-1664. [PMID: 34110579 DOI: 10.1007/s11030-021-10237-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.
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Jeon J, Kang S, Kim HU. Predicting biochemical and physiological effects of natural products from molecular structures using machine learning. Nat Prod Rep 2021; 38:1954-1966. [PMID: 34047331 DOI: 10.1039/d1np00016k] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.
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Affiliation(s)
- Junhyeok Jeon
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Seongmo Kang
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea and BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
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46
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Santana R, Onieva E, Zuluaga R, Duardo-Sánchez A, Gañán P. The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union. Curr Top Med Chem 2021; 21:828-838. [PMID: 33745436 DOI: 10.2174/1568026621666210319101847] [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: 10/23/2020] [Revised: 11/12/2020] [Accepted: 12/31/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process. METHODS In terms of methodology, a dogmatic study of the European regulation and the guidance of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations. RESULTS As a result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information. CONCLUSION It is concluded that Machine Learning has the capacity to help improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of the art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods, such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations, and European Authority Medicine. To our best knowledge, this is the first study focused on nanotechnology medicine products and machine learning used to support technical European public assessment reports (EPAR) for complementary information.
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Affiliation(s)
- Ricardo Santana
- DeustoTech-Fundacion Deusto, Avda. Universidades, 24,48007 Bilbao, Spain
| | - Enrique Onieva
- DeustoTech-Fundacion Deusto, Avda. Universidades, 24,48007 Bilbao, Spain
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana UPB050031, Medellin, Colombia
| | - Aliuska Duardo-Sánchez
- Department of Public Law, Law and the Human Genome Research Group, University of the Basque Country UPV/EHU 48940, Leioa, Biscay, Spain
| | - Piedad Gañán
- Facultad de Ingenieria Quimica, Universidad Pontificia Bolivariana UPB050031, Medellin, Colombia
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47
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Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs. Pharmaceuticals (Basel) 2021; 14:ph14050472. [PMID: 34067565 PMCID: PMC8156202 DOI: 10.3390/ph14050472] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/07/2021] [Accepted: 05/13/2021] [Indexed: 11/16/2022] Open
Abstract
Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug-drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.
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Shen WX, Zeng X, Zhu F, Wang YL, Qin C, Tan Y, Jiang YY, Chen YZ. Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00301-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Jiang D, Wu Z, Hsieh CY, Chen G, Liao B, Wang Z, Shen C, Cao D, Wu J, Hou T. Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. J Cheminform 2021; 13:12. [PMID: 33597034 PMCID: PMC7888189 DOI: 10.1186/s13321-020-00479-8] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 11/26/2020] [Indexed: 12/31/2022] Open
Abstract
Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.![]()
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Affiliation(s)
- Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.,State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058, Zhejiang, China.,College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory Tencent, Shenzhen, 518057, Guangdong, China
| | - Guangyong Chen
- Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, Guangdong, China
| | - Ben Liao
- Tencent Quantum Laboratory Tencent, Shenzhen, 518057, Guangdong, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410004, Hunan, China.
| | - Jian Wu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China. .,State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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Zhu K, Shen C, Tang C, Zhou Y, He C, Zuo Z. Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning. CHEMOSPHERE 2021; 265:129099. [PMID: 33272675 DOI: 10.1016/j.chemosphere.2020.129099] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 11/17/2020] [Accepted: 11/22/2020] [Indexed: 06/12/2023]
Abstract
The aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor, plays a crucial role in the regulation of xenobiotic metabolism. There are a large number of artificial or natural molecules in the environment that can activate AhR. In this study, we developed a virtual screening procedure to identify potential ligands of AhR. One structure-based method and two ligand-based methods were used for the virtual screening procedure. The results showed that the precision rate (0.96) and recall rate (0.64) of our procedure were significantly higher than those of a procedure used in a previous study, which suggests that supervised machine learning techniques can greatly improve the performance of virtual screening. Moreover, a pesticide dataset including 777 frequently used pesticides was screened. Seventy-seven pesticides were identified as potential AhR ligands by all three screening methods, among which 12 have never been previously reported as AhR agonists. Two non-agonist AhR ligands and 14 of the 77 pesticides were randomly selected for testing by in vitro and in vivo assays. All 14 pesticides showed different degrees of AhR agonistic activity, and none of the two non-agonist AhR ligand pesticides showed AhR agonistic activity, which suggests that our procedure had good robustness. Four of the pesticides were reported as AhR agonists for the first time, suggesting that these pesticides may need further toxicity assessment. In general, our procedure is a rapid, powerful and computationally inexpensive tool for predicting chemicals with AhR agonistic activity, which could be useful for environmental risk prediction and management.
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Affiliation(s)
- Kongyang Zhu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Chao Shen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Chen Tang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Yixi Zhou
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Chengyong He
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China.
| | - Zhenghong Zuo
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, Fujian, 361005, China.
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