1
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Ancajas CMF, Oyedele AS, Butt CM, Walker AS. Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products. Nat Prod Rep 2024. [PMID: 38912779 DOI: 10.1039/d4np00009a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.
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Affiliation(s)
| | | | - Caitlin M Butt
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
| | - Allison S Walker
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
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2
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Huang D, Xie J. EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction. Molecules 2024; 29:2912. [PMID: 38930976 PMCID: PMC11206982 DOI: 10.3390/molecules29122912] [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: 05/22/2024] [Revised: 06/15/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Accurately predicting drug-target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug-target affinity prediction have been treated as separate aspects of drug-target interaction, with few methods combining these tasks within a unified deep learning system to accelerate drug development. In this study, we propose EMPDTA, an end-to-end framework that integrates protein pocket prediction and drug-target affinity prediction to provide a comprehensive understanding of drug-target interactions. The EMPDTA framework consists of three main modules: pocket online detection, multimodal representation learning for affinity prediction, and multi-task joint training. The performance and potential of the proposed framework have been validated across diverse benchmark datasets, achieving robust results in both tasks. Furthermore, the visualization results of the predicted pockets demonstrate accurate pocket detection, confirming the effectiveness of our framework.
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Affiliation(s)
| | - Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;
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3
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Kang H, Wang J, Liu Y, Huang F, Zhou H, Xie X, Xu Q, Liang X, Xue X. Integrating UPLC-MS/MS with in Silico and in Vitro Screening Accelerates the Discovery of Active Compounds in Stephania epigaea. J Pharm Biomed Anal 2024; 248:116289. [PMID: 38901158 DOI: 10.1016/j.jpba.2024.116289] [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: 03/11/2024] [Revised: 05/18/2024] [Accepted: 06/04/2024] [Indexed: 06/22/2024]
Abstract
Traditional Chinese medicines (TCMs) are popular in clinic because of their safety and efficacy. They contain abundant natural active compounds, which are important sources of new drug discovery. However, how to efficiently identify active compounds from complex ingredients remains a challenge. In this study, a method combining UHPLC-MS/MS characterization and in silico screening was developed to discover compounds with dopamine D2 receptor (D2R) activity in Stephania epigaea (S. epigaea). By combining the compounds identified in S. epigaea by UHPLC-MS/MS with reported compounds, a virtual library of 80 compounds was constructed for in silico screening. Potentially active compounds were chosen based on screening scores and subsequently tested for in vitro activity on a transfected cell line CHO-K1-D2 model using label-free cellular phenotypic assay. Three D2R agonists and five D2R antagonists were identified. (-)-Asimilobine, N-nornuciferine and (-)-roemerine were reported for the first time as D2R agonists, with EC50 values of 0.35 ± 0.04 μM, 1.37 ± 0.10 μM and 0.82 ± 0.22 μM, respectively. Their target specificity was validated by desensitization and antagonism assay. (-)-Isocorypalmine, (-)-tetrahydropalmatine, (-)-discretine, (+)-corydaline and (-)-roemeroline showed strong antagonistic activity on D2R with IC50 values of 92 ± 9.9 nM, 1.73 ± 0.13 μM, 0.34 ± 0.02 μM, 2.09 ± 0.22 μM and 0.85 ± 0.08 μM, respectively. Their kinetic binding profiles were characterized using co-stimulation assay and they were both D2R competitive antagonists. We docked these ligands with human D2R crystal structure and analyzed the structure-activity relationship of aporphine-type D2R agonists and protoberberine-type D2R antagonists. These results would help to elucidate the mechanism of action of S. epigaea for its analgesic and sedative efficacy and benefit for D2R drug design. This study demonstrated the potential of integrating UHPLC-MS/MS with in silico and in vitro screening for accelerating the discovery of active compounds from TCMs.
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Affiliation(s)
- Hongjian Kang
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jixia Wang
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China.
| | - Yanfang Liu
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
| | - Feifei Huang
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
| | - Han Zhou
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
| | - Xiaomin Xie
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
| | - Qing Xu
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
| | - Xinmiao Liang
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
| | - Xingya Xue
- Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China.
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4
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Carlsson J, Luttens A. Structure-based virtual screening of vast chemical space as a starting point for drug discovery. Curr Opin Struct Biol 2024; 87:102829. [PMID: 38848655 DOI: 10.1016/j.sbi.2024.102829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 06/09/2024]
Abstract
Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.
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Affiliation(s)
- Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-751 24 Uppsala, Sweden.
| | - Andreas Luttens
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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5
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Bedart C, Simoben CV, Schapira M. Emerging structure-based computational methods to screen the exploding accessible chemical space. Curr Opin Struct Biol 2024; 86:102812. [PMID: 38603987 DOI: 10.1016/j.sbi.2024.102812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 04/13/2024]
Abstract
Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hitting high-quality compounds during virtual screening exercises but also poses new challenges as the number of chemically accessible molecules grows faster than the computing power necessary to screen them. We review here two novel approaches rapidly gaining in popularity to address this problem: machine learning-accelerated and synthon-based library screening. We summarize the results from seminal proof-of-concept studies, highlight the latest developments, and discuss limitations and future directions.
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Affiliation(s)
- Corentin Bedart
- Univ. Lille, Inserm, CHU Lille, U1286 - INFINITE - Institute for Translational Research in Inflammation, F-59000, Lille, France
| | - Conrad Veranso Simoben
- Structural Genomics Consortium, University of Toronto, 101 College Street, MaRS South Tower, Suite 700, Toronto, Ontario M5G 1L7, Canada
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto, 101 College Street, MaRS South Tower, Suite 700, Toronto, Ontario M5G 1L7, Canada; Department of Pharmacology and Toxicology, University of Toronto, 1 King's College Circle, Toronto, Ontario M5S 1A8, Canada.
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6
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Iram A, Dong Y, Ignea C. Synthetic biology advances towards a bio-based society in the era of artificial intelligence. Curr Opin Biotechnol 2024; 87:103143. [PMID: 38781699 DOI: 10.1016/j.copbio.2024.103143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 05/04/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024]
Abstract
Synthetic biology is a rapidly emerging field with broad underlying applications in health, industry, agriculture, or environment, enabling sustainable solutions for unmet needs of modern society. With the very recent addition of artificial intelligence (AI) approaches, this field is now growing at a rate that can help reach the envisioned goals of bio-based society within the next few decades. Integrating AI with plant-based technologies, such as protein engineering, phytochemicals production, plant system engineering, or microbiome engineering, potentially disruptive applications have already been reported. These include enzymatic synthesis of new-to-nature molecules, bioelectricity generation, or biomass applications as construction material. Thus, in the not-so-distant future, synthetic biologists will help attain the overarching goal of a sustainable yet efficient production system for every aspect of society.
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Affiliation(s)
- Attia Iram
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Yueming Dong
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Codruta Ignea
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada.
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7
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Bande AY, Baday S. Accelerating Molecular Docking using Machine Learning Methods. Mol Inform 2024; 43:e202300167. [PMID: 38850231 DOI: 10.1002/minf.202300167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/18/2023] [Indexed: 06/10/2024]
Abstract
Virtual screening (VS) is one of the well-established approaches in drug discovery which speeds up the search for a bioactive molecule and, reduces costs and efforts associated with experiments. VS helps to narrow down the search space of chemical space and allows selecting fewer and more probable candidate compounds for experimental testing. Docking calculations are one of the commonly used and highly appreciated structure-based drug discovery methods. Databases for chemical structures of small molecules have been growing rapidly. However, at the moment virtual screening of large libraries via docking is not very common. In this work, we aim to accelerate docking studies by predicting docking scores without explicitly performing docking calculations. We experimented with an attention based long short-term memory (LSTM) neural network for an efficient prediction of docking scores as well as other machine learning models such as XGBoost. By using docking scores of a small number of ligands we trained our models and predicted docking scores of a few million molecules. Specifically, we tested our approaches on 11 datasets that were produced from in-house drug discovery studies. On average, by training models using only 7000 molecules we predicted docking scores of approximately 3.8 million molecules with R2 (coefficient of determination) of 0.77 and Spearman rank correlation coefficient of 0.85. We designed the system with ease of use in mind. All the user needs to provide is a csv file containing SMILES and their respective docking scores, the system then outputs a model that the user can use for the prediction of docking score for a new molecule.
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Affiliation(s)
- Abdulsalam Y Bande
- Computer Science Department, Informatics Institute, Istanbul Technical University, Istanbul, Türkiye
| | - Sefer Baday
- Computer Science Department, Informatics Institute, Istanbul Technical University, Istanbul, Türkiye
- Applied Informatics Department, Informatics Institute, Istanbul Technical University, Istanbul, Türkiye
- Artificial Intelligence and Data Engineering Department, Faculty of Computer Informatics and Engineering, Istanbul Technical University, Istanbul, 34469, Türkiye
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8
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Wang L, Zhou Z, Yang X, Shi S, Zeng X, Cao D. The present state and challenges of active learning in drug discovery. Drug Discov Today 2024; 29:103985. [PMID: 38642700 DOI: 10.1016/j.drudis.2024.103985] [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: 03/03/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Active learning (AL) is an iterative feedback process that efficiently identifies valuable data within vast chemical space, even with limited labeled data. This characteristic renders it a valuable approach to tackle the ongoing challenges faced in drug discovery, such as the ever-expanding explore space and the limitations of labeled data. Consequently, AL is increasingly gaining prominence in the field of drug development. In this paper, we comprehensively review the application of AL at all stages of drug discovery, including compounds-target interaction prediction, virtual screening, molecular generation and optimization, as well as molecular properties prediction. Additionally, we discuss the challenges and prospects associated with the current applications of AL in drug discovery.
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Affiliation(s)
- Lei Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Zhenran Zhou
- Department of Computer Science, Hunan University, Changsha 410082, Hunan, China
| | - Xixi Yang
- Department of Computer Science, Hunan University, Changsha 410082, Hunan, China
| | - Shaohua Shi
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Xiangxiang Zeng
- Department of Computer Science, Hunan University, Changsha 410082, Hunan, China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China.
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9
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Matúška J, Bucinsky L, Gall M, Pitoňák M, Štekláč M. SchNetPack Hyperparameter Optimization for a More Reliable Top Docking Scores Prediction. J Phys Chem B 2024; 128:4943-4951. [PMID: 38733335 DOI: 10.1021/acs.jpcb.4c00296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
Options to improve the extrapolation power of the neural network designed using the SchNetPack package with respect to top docking scores prediction are presented. It is shown that hyperparameter tuning of the atomistic model representation (in the schnetpack.representation) improves the prediction of the top scoring compounds, which have characteristically a low incidence in randomized data sets for training of machine learning models. The prediction robustness is evaluated according to the mean square error (MSE) and the entropy of the average loss landscape decrease. Admittedly, the improvement of the top scoring compounds' prediction accuracy comes with the penalty of worsening the overall prediction power. It is revealed that the most impactful hyperparameter is the cutoff (5 Å is reported as the optimal choice). Other parameters (e.g., number of radial basis functions, number of interaction layers of the neural network, feature vector size or its batch size) are found to not affect the prediction robustness of the top scoring compounds in any comparable way relative to the cutoff. The MSE of the best docking score prediction (below -13 kcal/mol) improves from ca. 3.5 to 0.9 kcal/mol, while the prediction of less potent compounds (-13 to -11 kcal/mol) shows a lesser improvement, i.e., a decrease of MSE from 1.6 to 1.3 kcal/mol. Additionally, oversampling and undersampling of the training set with respect to the top scoring compounds' abundance is presented. The results indicate that the cutoff choice performs better than over- or undersampling of the training set, with undersampling performing better than oversampling.
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Affiliation(s)
- Ján Matúška
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
| | - Lukas Bucinsky
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
| | - Marián Gall
- Institute of Information Engineering, Automation and Mathematics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
- National SuperComputing Center, Dúbravská cesta č. 9, SK-84104 Bratislava, Slovak Republic
| | - Michal Pitoňák
- National SuperComputing Center, Dúbravská cesta č. 9, SK-84104 Bratislava, Slovak Republic
- Department of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská dolina Ilkovičova 6, SK-84215 Bratislava, Slovak Republic
| | - Marek Štekláč
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
- Computing Centre, Centre of Operations of the Slovak Academy of Sciences, Dúbravská cesta č. 9, SK-84535 Bratislava, Slovak Republic
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Monsia R, Bhattacharyya S. Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique. RESEARCH SQUARE 2024:rs.3.rs-4355625. [PMID: 38766198 PMCID: PMC11100899 DOI: 10.21203/rs.3.rs-4355625/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it. Scientific contribution We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery.
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11
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [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: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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Yang K, Xie Z, Li Z, Qian X, Sun N, He T, Xu Z, Jiang J, Mei Q, Wang J, Qu S, Xu X, Chen C, Ju B. MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery. J Chem Inf Model 2024; 64:2941-2947. [PMID: 38563534 DOI: 10.1021/acs.jcim.3c01979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.
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Affiliation(s)
- Keda Yang
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China
| | - Zewen Xie
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Zhen Li
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Xiaoliang Qian
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Nannan Sun
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Tao He
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Zuodong Xu
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Jing Jiang
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Qi Mei
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Jie Wang
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Shugang Qu
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Xiaoling Xu
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China
| | - Chaoxiang Chen
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China
| | - Bin Ju
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
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Marin E, Kovaleva M, Kadukova M, Mustafin K, Khorn P, Rogachev A, Mishin A, Guskov A, Borshchevskiy V. Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking. J Chem Inf Model 2024; 64:2612-2623. [PMID: 38157481 PMCID: PMC11005039 DOI: 10.1021/acs.jcim.3c01661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.
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Affiliation(s)
- Egor Marin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Margarita Kovaleva
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Maria Kadukova
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- University
Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Khalid Mustafin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Polina Khorn
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Andrey Rogachev
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Joint
Institute for Nuclear Research, Dubna 141980, Russian
Federation
| | - Alexey Mishin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Albert Guskov
- Groningen
Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Valentin Borshchevskiy
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Joint
Institute for Nuclear Research, Dubna 141980, Russian
Federation
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14
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Roggia M, Natale B, Amendola G, Di Maro S, Cosconati S. Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach. J Chem Inf Model 2024; 64:2143-2149. [PMID: 37552222 PMCID: PMC11005044 DOI: 10.1021/acs.jcim.3c00647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Indexed: 08/09/2023]
Abstract
The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual screening campaigns for drug discovery. By implementing PyRMD2Dock, we demonstrate that it is possible to rapidly screen massive chemical databases and identify those with the highest predicted binding affinity to a target protein. Our benchmarking and screening experiments illustrate the predictive power and speed of PyRMD2Dock and highlight its potential to accelerate the discovery of novel drug candidates. Overall, this study showcases the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultralarge molecular databases in drug discovery. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.
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Affiliation(s)
- Michele Roggia
- DiSTABiF, University
of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy
| | - Benito Natale
- DiSTABiF, University
of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy
| | - Giorgio Amendola
- DiSTABiF, University
of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy
| | - Salvatore Di Maro
- DiSTABiF, University
of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy
| | - Sandro Cosconati
- DiSTABiF, University
of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy
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15
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Kumar N, Srivastava R. Deep learning in structural bioinformatics: current applications and future perspectives. Brief Bioinform 2024; 25:bbae042. [PMID: 38701422 PMCID: PMC11066934 DOI: 10.1093/bib/bbae042] [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: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/18/2024] [Indexed: 05/05/2024] Open
Abstract
In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.
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Affiliation(s)
- Niranjan Kumar
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Rakesh Srivastava
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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16
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Sindt F, Seyller A, Eguida M, Rognan D. Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces. ACS CENTRAL SCIENCE 2024; 10:615-627. [PMID: 38559302 PMCID: PMC10979501 DOI: 10.1021/acscentsci.3c01521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 04/04/2024]
Abstract
Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.
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Affiliation(s)
- François Sindt
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | - Anthony Seyller
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | | | - Didier Rognan
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
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17
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Cao Z, Sciabola S, Wang Y. Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening. J Chem Inf Model 2024; 64:1882-1891. [PMID: 38442000 DOI: 10.1021/acs.jcim.3c01938] [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: 03/07/2024]
Abstract
Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.
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Affiliation(s)
- Zhonglin Cao
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
| | - Simone Sciabola
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
| | - Ye Wang
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
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18
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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19
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Cheng C, Beroza P. Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces. J Chem Inf Model 2024; 64:1251-1260. [PMID: 38335044 DOI: 10.1021/acs.jcim.3c01865] [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: 02/12/2024]
Abstract
Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.
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Affiliation(s)
- Chen Cheng
- Discovery Chemistry, Genentech, South San Francisco, California 94080, United States
| | - Paul Beroza
- Discovery Chemistry, Genentech, South San Francisco, California 94080, United States
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20
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Nam K, Shao Y, Major DT, Wolf-Watz M. Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development. ACS OMEGA 2024; 9:7393-7412. [PMID: 38405524 PMCID: PMC10883025 DOI: 10.1021/acsomega.3c09084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 02/27/2024]
Abstract
Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.
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Affiliation(s)
- Kwangho Nam
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
| | - Yihan Shao
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019-5251, United States
| | - Dan T. Major
- Department
of Chemistry and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel
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21
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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22
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Cichońska A, Ravikumar B, Rahman R. AI for targeted polypharmacology: The next frontier in drug discovery. Curr Opin Struct Biol 2024; 84:102771. [PMID: 38215530 DOI: 10.1016/j.sbi.2023.102771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/14/2024]
Abstract
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.
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23
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Sulimov AV, Ilin IS, Tashchilova AS, Kondakova OA, Kutov DC, Sulimov VB. Docking and other computing tools in drug design against SARS-CoV-2. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:91-136. [PMID: 38353209 DOI: 10.1080/1062936x.2024.2306336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 02/16/2024]
Abstract
The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.
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Affiliation(s)
- A V Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - I S Ilin
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - A S Tashchilova
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - O A Kondakova
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - D C Kutov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - V B Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
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24
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Cai H, Shen C, Jian T, Zhang X, Chen T, Han X, Yang Z, Dang W, Hsieh CY, Kang Y, Pan P, Ji X, Song J, Hou T, Deng Y. CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training. Chem Sci 2024; 15:1449-1471. [PMID: 38274053 PMCID: PMC10806797 DOI: 10.1039/d3sc05552c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep learning model for the prediction of protein-ligand atomic distance matrices, and a translation, rotation and torsion-guided geometry optimization procedure to reconstruct the matrices into a credible binding pose. The pre-training and multiple innovative architectural designs facilitate the dramatically improved docking accuracy of our approach over the baselines in terms of multiple docking scenarios, thereby contributing to its outstanding early recognition performance in several retrospective virtual screening campaigns. Further explorations demonstrate that CarsiDock can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.
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Affiliation(s)
- Heng Cai
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Chao Shen
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tianye Jian
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tong Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xiaoqi Han
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Zhuo Yang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Wei Dang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Chang-Yu Hsieh
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiangyang Ji
- Department of Automation, Tsinghua University Beijing 100084 China
| | - Jianfei Song
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Tingjun Hou
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
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25
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Sharo C, Zhai T, Huang Z. Investigation of Potential Drug Targets Involved in Inflammation Contributing to Alzheimer's Disease Progression. Pharmaceuticals (Basel) 2024; 17:137. [PMID: 38276010 PMCID: PMC10819325 DOI: 10.3390/ph17010137] [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: 04/01/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
Alzheimer's disease has become a major public health issue. While extensive research has been conducted in the last few decades, few drugs have been approved by the FDA to treat Alzheimer's disease. There is still an urgent need for understanding the disease pathogenesis, as well as identifying new drug targets for further drug discovery. Alzheimer's disease is known to arise from a build-up of amyloid beta (Aβ) plaques as well as tangles of tau proteins. Along similar lines to Alzheimer's disease, inflammation in the brain is known to stem from the degeneration of tissue and build-up of insoluble materials. A minireview was conducted in this work assessing the genes, proteins, reactions, and pathways that link brain inflammation and Alzheimer's disease. Existing tools in Systems Biology were implemented to build protein interaction networks, mainly for the classical complement pathway and G protein-coupled receptors (GPCRs), to rank the protein targets according to their interactions. The top 10 protein targets were mainly from the classical complement pathway. With the consideration of existing clinical trials and crystal structures, proteins C5AR1 and GARBG1 were identified as the best targets for further drug discovery, through computational approaches like ligand-protein docking techniques.
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Affiliation(s)
| | | | - Zuyi Huang
- Department of Chemical and Biological Engineering, Villanova University, Villanova, PA 19085, USA
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26
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Rudrapal M, Kirboga KK, Abdalla M, Maji S. Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints. Mol Divers 2024:10.1007/s11030-023-10782-9. [PMID: 38200203 DOI: 10.1007/s11030-023-10782-9] [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: 07/11/2023] [Accepted: 11/22/2023] [Indexed: 01/12/2024]
Abstract
Cyclooxygenase-2 (COX-2) inhibitors are nonsteroidal anti-inflammatory drugs that treat inflammation, pain and fever. This study determined the interaction mechanisms of COX-2 inhibitors and the molecular properties needed to design new drug candidates. Using machine learning and explainable AI methods, the inhibition activity of 1488 molecules was modelled, and essential properties were identified. These properties included aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. They affected the water solubility, hydrophobicity and binding affinity of COX-2 inhibitors. The binding mode, stability and ADME properties of 16 ligands bound to the Cyclooxygenase active site of COX-2 were investigated by molecular docking, molecular dynamics simulation and MM-GBSA analysis. The results showed that ligand 339,222 was the most stable and effective COX-2 inhibitor. It inhibited prostaglandin synthesis by disrupting the protein conformation of COX-2. It had good ADME properties and high clinical potential. This study demonstrated the potential of machine learning and bioinformatics methods in discovering COX-2 inhibitors.
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Affiliation(s)
- Mithun Rudrapal
- Department of Pharmaceutical Sciences, School of Biotechnology and Pharmaceutical Sciences, Vignan's Foundation for Science, Technology & Research (Deemed to Be University), Guntur, 522213, India.
| | - Kevser Kübra Kirboga
- Informatics Institute, Istanbul Technical University, 34469, Maslak, Istanbul, Turkey.
- Bioengineering Department, BilecikSeyhEdebali University, 11230, Bilecik, Turkey.
| | - Mohnad Abdalla
- Pediatric Research Institute, Children's Hospital Affiliated to Shandong University, Jinan, 250022, Shandong, People's Republic of China
| | - Siddhartha Maji
- Department of Chemistry, Oklahoma State University, Stillwater, OK, USA
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27
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Chandra R, Horne RI, Vendruscolo M. Bayesian Optimization in the Latent Space of a Variational Autoencoder for the Generation of Selective FLT3 Inhibitors. J Chem Theory Comput 2024; 20:469-476. [PMID: 38112559 PMCID: PMC10782437 DOI: 10.1021/acs.jctc.3c01224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023]
Abstract
The process of drug design requires the initial identification of compounds that bind their targets with high affinity and selectivity. Advances in generative modeling of small molecules based on deep learning are offering novel opportunities for making this process faster and cheaper. Here, we propose an approach to achieve this goal, where predictions of binding affinity are used in conjunction with the Junction Tree Variational Autoencoder (JTVAE) whose latent space is used to facilitate the efficient exploration of the chemical space using a Bayesian optimization strategy. The exploration identifies small molecules predicted to have both high affinity and high selectivity by using an objective function that optimizes the binding to the target while penalizing the binding to off-targets. The framework is demonstrated for FMS-like tyrosine kinase 3 (FLT3) and shown to predict small molecules with predicted affinity and selectivity comparable to those of clinically approved drugs for this target.
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Affiliation(s)
- Raghav Chandra
- Centre for Misfolding Diseases,
Yusuf Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, U.K.
| | - Robert I. Horne
- Centre for Misfolding Diseases,
Yusuf Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, U.K.
| | - Michele Vendruscolo
- Centre for Misfolding Diseases,
Yusuf Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, U.K.
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28
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Flachsenberg F, Ehrt C, Gutermuth T, Rarey M. Redocking the PDB. J Chem Inf Model 2024; 64:219-237. [PMID: 38108627 DOI: 10.1021/acs.jcim.3c01573] [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: 12/19/2023]
Abstract
Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).
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Affiliation(s)
- Florian Flachsenberg
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Torben Gutermuth
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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29
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Melancon K, Pliushcheuskaya P, Meiler J, Künze G. Targeting ion channels with ultra-large library screening for hit discovery. Front Mol Neurosci 2024; 16:1336004. [PMID: 38249296 PMCID: PMC10796734 DOI: 10.3389/fnmol.2023.1336004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.
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Affiliation(s)
- Kortney Melancon
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | | | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
| | - Georg Künze
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
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30
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Popov KI, Wellnitz J, Maxfield T, Tropsha A. HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries. Mol Inform 2024; 43:e202300207. [PMID: 37802967 PMCID: PMC11156482 DOI: 10.1002/minf.202300207] [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: 08/16/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 10/08/2023]
Abstract
Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.
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Affiliation(s)
- Konstantin I. Popov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - James Wellnitz
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - Travis Maxfield
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
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31
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Abou Hajal A, Al Meslamani AZ. Insights into artificial intelligence utilisation in drug discovery. J Med Econ 2024; 27:304-308. [PMID: 38385328 DOI: 10.1080/13696998.2024.2315864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 02/05/2024] [Indexed: 02/23/2024]
Affiliation(s)
- Abdallah Abou Hajal
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
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32
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Narykov O, Zhu Y, Brettin T, Evrard YA, Partin A, Shukla M, Xia F, Clyde A, Vasanthakumari P, Doroshow JH, Stevens RL. Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models. Cancers (Basel) 2023; 16:50. [PMID: 38201477 PMCID: PMC10777918 DOI: 10.3390/cancers16010050] [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: 11/03/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 01/12/2024] Open
Abstract
Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.
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Affiliation(s)
- Oleksandr Narykov
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Yitan Zhu
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Thomas Brettin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Yvonne A. Evrard
- Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA;
| | - Alexander Partin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Maulik Shukla
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Fangfang Xia
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Austin Clyde
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA
| | - Priyanka Vasanthakumari
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - James H. Doroshow
- Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD 20892, USA;
| | - Rick L. Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA
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33
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Venianakis T, Primikyri A, Opatz T, Petry S, Papamokos G, Gerothanassis IP. NMR and Docking Calculations Reveal Novel Atomistic Selectivity of a Synthetic High-Affinity Free Fatty Acid vs. Free Fatty Acids in Sudlow's Drug Binding Sites in Human Serum Albumin. Molecules 2023; 28:7991. [PMID: 38138481 PMCID: PMC10745614 DOI: 10.3390/molecules28247991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
Saturation transfer difference (STD), inter-ligand NOEs (INPHARMA NMR), and docking calculations are reported for investigating specific binding sites of the high-affinity synthetic 7-nitrobenz-2-oxa-1,3-diazoyl-4-C12 fatty acid (NBD-C12 FA) with non-labeled human serum albumin (HSA) and in competition with the drugs warfarin and ibuprofen. A limited number of negative interligand NOEs between NBD-C12 FA and warfarin were interpreted in terms of a short-range allosteric competitive binding in the wide Sudlow's binding site II (FA7) of NBD-C12 FA with Ser-202, Lys-199, and Trp-214 and warfarin with Arg-218 and Arg-222. In contrast, the significant number of interligand NOEs between NBD-C12 FA and ibuprofen were interpreted in terms of a competitive binding mode in Sudlow's binding site I (FA3 and FA4) with Ser-342, Arg-348, Arg-485, Arg-410, and Tyr-411. NBD-C12 FA has the unique structural properties, compared to short-, medium-, and long-chain saturated and unsaturated natural free fatty acids, of interacting with well-defined structures with amino acids of both the internal and external polar anchor sites in Sudlow's binding site I and with amino acids in both FA3 and FA4 in Sudlow's binding site II. The NBD-C12 FA, therefore, interacts with novel structural characteristics in the drug binding sites I and II and can be regarded as a prototype molecule for drug development.
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Affiliation(s)
- Themistoklis Venianakis
- Section of Organic Chemistry and Biochemistry, Department of Chemistry, University of Ioannina, GR-45110 Ioannina, Greece; (T.V.); (A.P.)
| | - Alexandra Primikyri
- Section of Organic Chemistry and Biochemistry, Department of Chemistry, University of Ioannina, GR-45110 Ioannina, Greece; (T.V.); (A.P.)
| | - Till Opatz
- Department of Chemistry, Johannes Gutenberg-University, Duesbergweg, 10–14, 55128 Mainz, Germany;
| | - Stefan Petry
- Sanofi-Aventis Deutschland GmbH, Integrated Drug Discovery, Industriepark Höchst, 65926 Frankfurt am Main, Germany;
| | - Georgios Papamokos
- Section of Organic Chemistry and Biochemistry, Department of Chemistry, University of Ioannina, GR-45110 Ioannina, Greece; (T.V.); (A.P.)
- Department of Physics, Harvard University, 17 Oxford Street, Cambridge, MA 02138, USA
| | - Ioannis P. Gerothanassis
- Section of Organic Chemistry and Biochemistry, Department of Chemistry, University of Ioannina, GR-45110 Ioannina, Greece; (T.V.); (A.P.)
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34
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Viswanathan K, Goel M, Laghuvarapu S, Varma G, Priyakumar UD. Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines. Sci Rep 2023; 13:21069. [PMID: 38030689 PMCID: PMC10686981 DOI: 10.1038/s41598-023-42952-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 09/16/2023] [Indexed: 12/01/2023] Open
Abstract
The discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with [Formula: see text]a 70% improvement in runtime compared to the baseline model by labeling only [Formula: see text]30% of molecules compared to the baseline oracle.
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Affiliation(s)
- Karthik Viswanathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Manan Goel
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Girish Varma
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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35
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Dong T, Yang Z, Zhou J, Chen CYC. Equivariant Flexible Modeling of the Protein-Ligand Binding Pose with Geometric Deep Learning. J Chem Theory Comput 2023; 19:8446-8459. [PMID: 37938978 DOI: 10.1021/acs.jctc.3c00273] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Flexible modeling of the protein-ligand complex structure is a fundamental challenge for in silico drug development. Recent studies have improved commonly used docking tools by incorporating extra-deep learning-based steps. However, such strategies limit their accuracy and efficiency because they retain massive sampling pressure and lack consideration for flexible biomolecular changes. In this study, we propose FlexPose, a geometric graph network capable of direct flexible modeling of complex structures in Euclidean space without the following conventional sampling and scoring strategies. Our model adopts two key designs: scalar-vector dual feature representation and SE(3)-equivariant network, to manage dynamic structural changes, as well as two strategies: conformation-aware pretraining and weakly supervised learning, to boost model generalizability in unseen chemical space. Benefiting from these paradigms, our model dramatically outperforms all tested popular docking tools and recently advanced deep learning methods, especially in tasks involving protein conformation changes. We further investigate the impact of protein and ligand similarity on the model performance with two conformation-aware strategies. Moreover, FlexPose provides an affinity estimation and model confidence for postanalysis.
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Affiliation(s)
- Tiejun Dong
- Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 510275, China
| | - Ziduo Yang
- Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 510275, China
| | - Jun Zhou
- Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 510275, China
| | - Calvin Yu-Chian Chen
- Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 510275, China
- AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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36
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Yu L, He X, Fang X, Liu L, Liu J. Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening. J Chem Inf Model 2023; 63:6501-6514. [PMID: 37882338 DOI: 10.1021/acs.jcim.3c01371] [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: 10/27/2023]
Abstract
Structure-based virtual screening has been a crucial tool in drug discovery for decades. However, as the chemical space expands, the existing structure-based virtual screening techniques based on molecular docking and scoring struggle to handle billion-entry ultralarge libraries due to the high computational cost. To address this challenge, people have resorted to machine learning techniques to enhance structure-based virtual screening for efficiently exploring the vast chemical space. In those cases, compounds are usually treated as sequential strings or two-dimensional topology graphs, limiting their ability to incorporate three-dimensional structural information for downstream tasks. We herein propose a novel deep learning protocol, GEM-Screen, which utilizes the geometry-enhanced molecular representation of the compounds docking to a specific target and is trained on docking scores of a small fraction of a library through an active learning strategy to approximate the docking outcome for yet nontraining entries. This protocol is applied to virtual screening campaigns against the AmpC and D4 targets, demonstrating that GEM-Screen enriches more than 90% of the hit scaffolds for AmpC in the top 4% of model predictions and more than 80% of the hit scaffolds for D4 in the same top-ranking size of library. GEM-Screen can be used in conjunction with traditional docking programs for docking of only the top-ranked compounds to avoid the exhaustive docking of the whole library, thus allowing for discovering top-scoring compounds from billion-entry libraries in a rapid yet accurate fashion.
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Affiliation(s)
- Lan Yu
- School of Science, China Pharmaceutical University, Nanjing 210009, China
| | - Xiao He
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200062, China
| | - Xiaomin Fang
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen 518063, China
| | - Lihang Liu
- Baidu International Technology (Shenzhen) Co., Ltd., Shenzhen 518063, China
| | - Jinfeng Liu
- School of Science, China Pharmaceutical University, Nanjing 210009, China
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
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37
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Hou Y, Bai Y, Lu C, Wang Q, Wang Z, Gao J, Xu H. Applying molecular docking to pesticides. PEST MANAGEMENT SCIENCE 2023; 79:4140-4152. [PMID: 37547967 DOI: 10.1002/ps.7700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/17/2023] [Accepted: 08/05/2023] [Indexed: 08/08/2023]
Abstract
Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yang Hou
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yuqian Bai
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Chang Lu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Qiuchan Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Zishi Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Jinsheng Gao
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Hongliang Xu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
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38
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Monari L, Galentino K, Cecchini M. ChemFlow_py: a flexible toolkit for docking and rescoring. J Comput Aided Mol Des 2023; 37:565-572. [PMID: 37620503 DOI: 10.1007/s10822-023-00527-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .
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Affiliation(s)
- Luca Monari
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Katia Galentino
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Marco Cecchini
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France.
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Sivula T, Yetukuri L, Kalliokoski T, Käsnänen H, Poso A, Pöhner I. Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries. J Chem Inf Model 2023; 63:5773-5783. [PMID: 37655823 PMCID: PMC10523430 DOI: 10.1021/acs.jcim.3c01239] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Indexed: 09/02/2023]
Abstract
The emergence of ultra-large screening libraries, filled to the brim with billions of readily available compounds, poses a growing challenge for docking-based virtual screening. Machine learning (ML)-boosted strategies like the tool HASTEN combine rapid ML prediction with the brute-force docking of small fractions of such libraries to increase screening throughput and take on giga-scale libraries. In our case study of an anti-bacterial chaperone and an anti-viral kinase, we first generated a brute-force docking baseline for 1.56 billion compounds in the Enamine REAL lead-like library with the fast Glide high-throughput virtual screening protocol. With HASTEN, we observed robust recall of 90% of the true 1000 top-scoring virtual hits in both targets when docking only 1% of the entire library. This reduction of the required docking experiments by 99% significantly shortens the screening time. In the kinase target, the employment of a hydrogen bonding constraint resulted in a major proportion of unsuccessful docking attempts and hampered ML predictions. We demonstrate the optimization potential in the treatment of failed compounds when performing ML-boosted screening and benchmark and showcase HASTEN as a fast and robust tool in a growing arsenal of approaches to unlock the chemical space covered by giga-scale screening libraries for everyday drug discovery campaigns.
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Affiliation(s)
- Toni Sivula
- School
of Pharmacy, University of Eastern Finland, Kuopio FI-70211, Finland
| | | | - Tuomo Kalliokoski
- Computational
Medicine Design, Orion Pharma, Orionintie 1A, Espoo FI-02101, Finland
| | - Heikki Käsnänen
- Computational
Medicine Design, Orion Pharma, Orionintie 1A, Espoo FI-02101, Finland
| | - Antti Poso
- School
of Pharmacy, University of Eastern Finland, Kuopio FI-70211, Finland
- Department
of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical
Sciences, Eberhard Karls University, Tübingen DE-72076, Germany
- Cluster
of Excellence iFIT (EXC 2180) “Image-Guided and Functionally
Instructed Tumor Therapies”, University
of Tübingen, Tübingen DE-72076, Germany
- Tübingen
Center for Academic Drug Discovery & Development (TüCAD2), Tübingen DE-72076, Germany
| | - Ina Pöhner
- School
of Pharmacy, University of Eastern Finland, Kuopio FI-70211, Finland
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Kamerlin SCL. Progress in using deep learning to treat cancer. NATURE COMPUTATIONAL SCIENCE 2023; 3:739-740. [PMID: 38177785 DOI: 10.1038/s43588-023-00514-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
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41
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An Y, Glavatskikh M, Lim J, Wang X, Norris-Drouin J, Hardy PB, Leisner TM, Pearce KH, Kireev D. Machine Learning-driven Fragment-based Discovery of CIB1-directed Anti-Tumor Agents by FRASE-bot. RESEARCH SQUARE 2023:rs.3.rs-3197490. [PMID: 37645935 PMCID: PMC10462244 DOI: 10.21203/rs.3.rs-3197490/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify. Novel therapeutic targets are often hard-to-drug proteins, such as messengers or transcription factors. Computational strategies arise as a promising solution to expedite drug discovery for unconventional therapeutic targets. FRASE-bot exploits big data and machine learning (ML) to distill 3D information relevant to the target protein from thousands of protein-ligand complexes to seed it with ligand fragments. The seeded fragments can then inform either (i) de novo design of 3D ligand structures or (ii) ultra-large-scale virtual screening of commercially available compounds. Here, FRASE-bot was applied to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising but ligand-orphan drug target implicated in triple negative breast cancer. The signaling function of CIB1 relies on protein-protein interactions and its structure does not feature any natural ligand-binding pocket. FRASE-based virtual screening identified the first small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depleted cells.
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Affiliation(s)
- Yi An
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Marta Glavatskikh
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Jiwoong Lim
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Xiaowen Wang
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
- Chemistry department, University of Missouri, Columbia, Columbia, MO, 65211
| | - Jacqueline Norris-Drouin
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - P. Brian Hardy
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Tina M. Leisner
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Kenneth H. Pearce
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
| | - Dmitri Kireev
- Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513
- Chemistry department, University of Missouri, Columbia, Columbia, MO, 65211
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42
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Nyathi B, Bvunzawabaya JT, Venissa P Mudawarima C, Manzombe E, Tsotsoro K, Selemani MA, Munyuki G, Rwere F. Inhibitory and in silico molecular docking of Xeroderris stuhlmannii (Taub.) Mendonca & E.P. Sousa phytochemical compounds on human α-glucosidases. JOURNAL OF ETHNOPHARMACOLOGY 2023; 312:116501. [PMID: 37100261 DOI: 10.1016/j.jep.2023.116501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/19/2023] [Accepted: 04/13/2023] [Indexed: 05/08/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Herbal traditional medicine is used by millions of people in Africa for treatment of ailments such as diabetes mellitus, stomach disorders and respiratory diseases. Xeroderris stuhlmannii (Taub.) Mendonca & E.P. Sousa (X. stuhlmannii (Taub.)) is a medicinal plant used traditionally in Zimbabwe to treat type 2 diabetes mellitus (T2DM) and its complications. However, there is no scientific evidence to support its inhibitory effect against digestive enzymes (α-glucosidases) that are linked to high blood sugar in humans. AIM OF THE STUDY This work aims to investigate whether bioactive phytochemicals of crude X. stuhlmannii (Taub.) can scavenge free radicals and inhibit α-glucosidases in order to reduce blood sugar in humans. MATERIALS AND METHODS Here we examined the free radical scavenging potential of crude aqueous, ethyl acetate and methanolic extracts of X. stuhlmannii (Taub.) using the diphenyl-2-picrylhydrazyl assay in vitro. Furthermore, we carried out in vitro inhibition of α-glucosidases (α-amylase and α-glucosidase) by the crude extracts using chromogenic 3,5-dinitrosalicylic acid and p-nitrophenyl-α-D-glucopyranoside substrates. We also used molecular docking approaches (Autodock Vina) to screen for bioactive phytochemical compounds targeting the digestive enzymes. RESULTS Our results showed that phytochemicals in X. stuhlmannii (Taub.) aqueous, ethyl acetate and methanolic extracts scavenged free radicals with IC50 values ranging from 0.002 to 0.013 μg/mL. Furthermore, crude aqueous, ethyl acetate and methanolic extracts significantly inhibited α-amylase and α-glucosidase with IC50 values of 10.5-29.5 μg/mL (versus 54.1 ± 0.7 μg/mL for acarbose) and 8.8-49.5 μg/mL (versus 161.4 ± 1.8 μg/mL for acarbose), respectively. In silico molecular docking findings and pharmacokinetic predictions showed that myricetin is likely a novel plant-derived α-glucosidase inhibitor. CONCLUSION Collectively, our findings suggest pharmacological targeting of digestive enzymes by X. stuhlmannii (Taub.) crude extracts may reduce blood sugar in humans with T2DM via inhibition of α-glucosidases.
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Affiliation(s)
- Brilliant Nyathi
- Department of Chemistry, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe
| | - Jonathan Tatenda Bvunzawabaya
- Department of Chemistry, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe; Department of Chemical Sciences, Faculty of Science and Technology Midlands State University, Private Bag 9055 Senga Road, Gweru, 263, Zimbabwe
| | - Chido Venissa P Mudawarima
- Department of Chemistry, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe
| | - Emily Manzombe
- Department of Chemistry, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe
| | - Kudakwashe Tsotsoro
- Department of Chemistry, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe
| | - Major Allen Selemani
- Department of Chemistry, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe
| | - Gadzikano Munyuki
- Department of Chemistry, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe
| | - Freeborn Rwere
- Department of Chemistry, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
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Abstract
Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.
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Affiliation(s)
- Christoph Gorgulla
- Harvard Medical School and Physics Department, Harvard University, Boston, Massachusetts, USA;
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Current affiliation: Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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Radaeva M, Morin H, Pandey M, Ban F, Guo M, LeBlanc E, Lallous N, Cherkasov A. Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening. Mol Inform 2023; 42:e2300026. [PMID: 37193651 DOI: 10.1002/minf.202300026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 05/18/2023]
Abstract
Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.
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Affiliation(s)
- Mariia Radaeva
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Helene Morin
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Mohit Pandey
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Fuqiang Ban
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Maria Guo
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Eric LeBlanc
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Nada Lallous
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
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45
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Ruth M, Gerbig D, Schreiner PR. Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies. J Chem Theory Comput 2023. [PMID: 37418619 DOI: 10.1021/acs.jctc.3c00274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely useful, and, with increasing computational power, the limits of high-level approaches (such as coupled cluster theory) are expanding to ever larger systems. However, because scaling is highly unfavorable, these methods are still not universally applicable to larger systems. To address the need for fast and accurate electronic energies of larger systems, we created a database of around 8000 small organic monomers (2000 dimers) optimized at the B3LYP-D3(BJ)/cc-pVTZ level of theory. This database also includes single-point energies computed at various levels of theory, including PBE1PBE, ωΒ97Χ, M06-2X, revTPSS, B3LYP, and BP86, for density functional theory as well as DLPNO-CCSD(T) and CCSD(T) for coupled cluster theory, all in conjunction with a cc-pVTZ basis. We used this database to train machine learning models based on graph neural networks using two different graph representations. Our models are able to make energy predictions from B3LYP-D3(BJ)/cc-pVTZ inputs to CCSD(T)/cc-pVTZ outputs with a mean absolute error of 0.78 and to DLPNO-CCSD(T)/cc-pVTZ with an mean absolute error of 0.50 and 0.18 kcal mol-1 for monomers and dimers, respectively. The model for dimers was further validated on the S22 database, and the monomer model was tested on challenging systems, including those with highly conjugated or functionally complex molecules.
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Affiliation(s)
- Marcel Ruth
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Dennis Gerbig
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Peter R Schreiner
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
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46
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Jin W, Zhang J, Chen X, Yin S, Yu H, Gao F, Yao D. Unraveling the complexity of histone-arginine methyltransferase CARM1 in cancer: From underlying mechanisms to targeted therapeutics. Biochim Biophys Acta Rev Cancer 2023; 1878:188916. [PMID: 37196782 DOI: 10.1016/j.bbcan.2023.188916] [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: 03/09/2023] [Revised: 04/28/2023] [Accepted: 05/12/2023] [Indexed: 05/19/2023]
Abstract
Coactivator-associated arginine methyltransferase 1 (CARM1), a type I protein arginine methyltransferase (PRMT), has been widely reported to catalyze arginine methylation of histone and non-histone substrates, which is closely associated with the occurrence and progression of cancer. Recently, accumulating studies have demonstrated the oncogenic role of CARM1 in many types of human cancers. More importantly, CARM1 has been emerging as an attractive therapeutic target for discovery of new candidate anti-tumor drugs. Therefore, in this review, we summarize the molecular structure of CARM1 and its key regulatory pathways, as well as further discuss the rapid progress in better understanding of the oncogenic functions of CARM1. Moreover, we further demonstrate several representative targeted CARM1 inhibitors, especially focusing on demonstrating their designing strategies and potential therapeutic applications. Together, these inspiring findings would shed new light on elucidating the underlying mechanisms of CARM1 and provide a clue on discovery of more potent and selective CARM1 inhibitors for the future targeted cancer therapy.
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Affiliation(s)
- Wenke Jin
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China; School of Pharmaceutical Sciences, Shenzhen Technology University, Shenzhen 518118, China; Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, and State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Jin Zhang
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China
| | - Xiya Chen
- School of Pharmaceutical Sciences, Shenzhen Technology University, Shenzhen 518118, China; School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China
| | - Siwen Yin
- School of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Haiyang Yu
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, and State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
| | - Feng Gao
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China.
| | - Dahong Yao
- School of Pharmaceutical Sciences, Shenzhen Technology University, Shenzhen 518118, China.
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47
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Lu Y, Yang Y, Zhu G, Zeng H, Fan Y, Guo F, Xu D, Wang B, Chen D, Ge G. Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer. Int J Biol Sci 2023; 19:3360-3382. [PMID: 37496997 PMCID: PMC10367563 DOI: 10.7150/ijbs.83026] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 06/13/2023] [Indexed: 07/28/2023] Open
Abstract
Targeted therapies in cancer treatment can improve in vivo efficacy and reduce adverse effects by altering the tissue exposure of specific biomolecules. However, there are still large number of target proteins in cancer are still undruggable, owing to the following factors including (1) lack of ligand-binding pockets, (2) function based on protein-protein interactions (PPIs), (3) the highly specific conserved active sites among protein family members, and (4) the variability of tertiary docking structures. The current status of undruggable targets proteins such as KRAS, TP53, C-MYC, PTP, are carefully introduced in this review. Some novel techniques and drug designing strategies have been applicated for overcoming these undruggable proteins, and the most classic and well-known technology is proteolysis targeting chimeras (PROTACs). In this review, the novel drug development strategies including targeting protein degradation, targeting PPI, targeting intrinsically disordered regions, as well as targeting protein-DNA binding are described, and we also discuss the potential of these strategies for overcoming the undruggable targets. Besides, intelligence-assisted technologies like Alpha-Fold help us a lot to predict the protein structure, which is beneficial for drug development. The discovery of new targets and the development of drugs targeting them, especially those undruggable targets, remain a huge challenge. New drug development strategies, better extraction processes that do not disrupt protein-protein interactions, and more precise artificial intelligence technologies may provide significant assistance in overcoming these undruggable targets.
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Affiliation(s)
- Yuqing Lu
- Dalian Medical University, 116044 Dalian City, Liaoning Province, China
| | - Yuewen Yang
- Dalian Medical University, 116044 Dalian City, Liaoning Province, China
| | - Guanghao Zhu
- Shanghai University of Traditional Chinese Medicine, 201203 Shanghai City, China
| | - Hairong Zeng
- Shanghai University of Traditional Chinese Medicine, 201203 Shanghai City, China
| | - Yiming Fan
- Dalian Harmony Medical Testing Laboratory Co., Ltd, 116620 Dalian City, Liaoning Province, China
| | - Fujia Guo
- Dalian Medical University, 116044 Dalian City, Liaoning Province, China
| | - Dongshu Xu
- Dalian Medical University, 116044 Dalian City, Liaoning Province, China
| | - Boya Wang
- Dalian Medical University, 116044 Dalian City, Liaoning Province, China
| | - Dapeng Chen
- Dalian Medical University, 116044 Dalian City, Liaoning Province, China
| | - Guangbo Ge
- Shanghai University of Traditional Chinese Medicine, 201203 Shanghai City, China
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48
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Thomson TM. On the importance for drug discovery of a transnational Latin American database of natural compound structures. Front Pharmacol 2023; 14:1207559. [PMID: 37426821 PMCID: PMC10324963 DOI: 10.3389/fphar.2023.1207559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Affiliation(s)
- Timothy M. Thomson
- Institute for Molecular Biology (IBMB-CSIC), Barcelona, Spain
- CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
- Universidad Peruana Cayetano Heredia, Lima, Peru
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49
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Zhang W, Zhang K, Huang J. A Simple Way to Incorporate Target Structural Information in Molecular Generative Models. J Chem Inf Model 2023. [PMID: 37318828 DOI: 10.1021/acs.jcim.3c00293] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Deep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. The method combines a message-passing neural network model that predicts docking scores with a generative neural network model as its reward function to navigate the chemical space searching for molecules that bind favorably with a specific target. A key feature of the method is the construction of target-specific molecular sets for training, designed to overcome potential transferability issues of surrogate docking models through a two-round training process. Consequently, this enables accurate guided exploration of the chemical space without reliance on the collection of prior knowledge about active and inactive compounds for the specific target. Tests on eight target proteins showed a 100-fold increase in hit generation compared to conventional docking calculations and the ability to generate molecules similar to approved drugs or known active ligands for specific targets without prior knowledge. This method provides a general and highly efficient solution for structure-based molecular generation.
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Affiliation(s)
- Wenyi Zhang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Kaiyue Zhang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Huang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
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50
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Pliushcheuskaya P, Künze G. Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels. Int J Mol Sci 2023; 24:ijms24119226. [PMID: 37298178 DOI: 10.3390/ijms24119226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.
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Affiliation(s)
- Palina Pliushcheuskaya
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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