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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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Qu X, Dong L, Luo D, Si Y, Wang B. Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2024; 64:2263-2274. [PMID: 37433009 DOI: 10.1021/acs.jcim.3c00567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Abstract
Water network rearrangement from the ligand-unbound state to the ligand-bound state is known to have significant effects on the protein-ligand binding interactions, but most of the current machine learning-based scoring functions overlook these effects. In this study, we endeavor to construct a comprehensive and realistic deep learning model by incorporating water network information into both ligand-unbound and -bound states. In particular, extended connectivity interaction features were integrated into graph representation, and graph transformer operator was employed to extract features of the ligand-unbound and -bound states. Through these efforts, we developed a water network-augmented two-state model called ECIFGraph::HM-Holo-Apo. Our new model exhibits satisfactory performance in terms of scoring, ranking, docking, screening, and reverse screening power tests on the CASF-2016 benchmark. In addition, it can achieve superior performance in large-scale docking-based virtual screening tests on the DEKOIS2.0 data set. Our study highlights that the use of a water network-augmented two-state model can be an effective strategy to bolster the robustness and applicability of machine learning-based scoring functions, particularly for targets with hydrophilic or solvent-exposed binding pockets.
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Affiliation(s)
- Xiaoyang Qu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Yubing Si
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, P. R. China
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Zhang Y, Li S, Meng K, Sun S. Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction. J Chem Inf Model 2024; 64:1456-1472. [PMID: 38385768 DOI: 10.1021/acs.jcim.3c01841] [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] [Indexed: 02/23/2024]
Abstract
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.
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Affiliation(s)
- Yunjiang Zhang
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Shuyuan Li
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Kong Meng
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Shaorui Sun
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
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Shahab M, Zheng G, Bin Jardan YA, Bourhia M. Machine learning and molecular simulation-based protocols to identify novel potential inhibitors for reverse transcriptase against HIV infections. J Biomol Struct Dyn 2024:1-14. [PMID: 38379294 DOI: 10.1080/07391102.2024.2319112] [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: 09/21/2023] [Accepted: 02/11/2024] [Indexed: 02/22/2024]
Abstract
Acquired immunodeficiency syndrome (AIDS) is a potentially fatal condition affecting the human immune system, which is attributed to the human immunodeficiency virus (HIV). The suppression of reverse transcriptase activity is a promising and feasible strategy for the therapeutic management of AIDS. In this study, we employed machine learning algorithms, such as support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF), and Gaussian naive base (GNB), which are fast and effective tools commonly used in drug design. For model training, we initially obtained a dataset of 5,159 compounds from BindingDB. The models were assessed using tenfold cross-validation to ensure their accuracy and reliability. Among these compounds, 1,645 compounds were labeled as active, having an IC50 below 0.49 µM, while 3,514 compounds were labeled "inactive against reverse transcriptase. Random forest achieved 86% accuracy on the train and test set among the different machine learning algorithms. Random forest model was then applied to an external ZINC dataset. Subsequently, only three hits-ZINC1359750464, ZINC1435357562, and ZINC1545719422-were selected based on the Lipinski Rule, docking score, and good interaction. The stability of these molecules was further evaluated by deploying molecular dynamics simulation and MM/GBSA, which were found to be -38.6013 ± 0.1103 kcal/mol for the Zidovudine/RT complex, -59.1761 ± 2.2926 kcal/mol for the ZINC1359750464/RT complex, -47.6292 ± 2.4206 kcal/mol for the ZINC1435357562/RT complex, and -50.7334 ± 2.5713 kcal/mol for the ZINC1545719422/RT complex.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Muhammad Shahab
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, PR China
| | - Guojun Zheng
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, PR China
| | - Yousef A Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Laboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco
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Gómez-Sacristán P, Simeon S, Tran-Nguyen VK, Patil S, Ballester PJ. Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers. J Adv Res 2024:S2090-1232(24)00037-7. [PMID: 38280715 DOI: 10.1016/j.jare.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/01/2023] [Accepted: 01/21/2024] [Indexed: 01/29/2024] Open
Abstract
INTRODUCTION Small-molecule Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD1/PDL1) inhibition via PDL1 dimerization has the potential to lead to inexpensive drugs with better cancer patient outcomes and milder side effects. However, this therapeutic approach has proven challenging, with only one PDL1 dimerizer reaching early clinical trials so far. There is hence a need for fast and accurate methods to develop alternative PDL1 dimerizers. OBJECTIVES We aim to show that structure-based virtual screening (SBVS) based on PDL1-specific machine-learning (ML) scoring functions (SFs) is a powerful drug design tool for detecting PD1/PDL1 inhibitors via PDL1 dimerization. METHODS By incorporating the latest MLSF advances, we generated and evaluated PDL1-specific MLSFs (classifiers and inactive-enriched regressors) on two demanding test sets. RESULTS 60 PDL1-specific MLSFs (30 classifiers and 30 regressors) were generated. Our large-scale analysis provides highly predictive PDL1-specific MLSFs that benefitted from training with large volumes of docked inactives and enabling inactive-enriched regression. CONCLUSION PDL1-specific MLSFs strongly outperformed generic SFs of various types on this target and are released here without restrictions.
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Affiliation(s)
| | - Saw Simeon
- Centre de Recherche en Cancérologie de Marseille, Marseille 13009, France
| | | | - Sachin Patil
- NanoBio Laboratory, Widener University, Chester, PA 19013, USA
| | - Pedro J Ballester
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
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Sharma V, Singh A, Chauhan S, Sharma PK, Chaudhary S, Sharma A, Porwal O, Fuloria NK. Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer. Curr Drug Deliv 2024; 21:870-886. [PMID: 37670704 DOI: 10.2174/1567201821666230905090621] [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: 10/31/2022] [Revised: 03/08/2023] [Accepted: 03/24/2023] [Indexed: 09/07/2023]
Abstract
Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.
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Affiliation(s)
- Vishal Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Amit Singh
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Sanjana Chauhan
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Pramod Kumar Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Shubham Chaudhary
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Astha Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Omji Porwal
- Department of Pharmacognosy, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
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Jin Z, Wei Z. Molecular simulation for food protein-ligand interactions: A comprehensive review on principles, current applications, and emerging trends. Compr Rev Food Sci Food Saf 2024; 23:e13280. [PMID: 38284571 DOI: 10.1111/1541-4337.13280] [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: 08/07/2023] [Revised: 11/19/2023] [Accepted: 11/22/2023] [Indexed: 01/30/2024]
Abstract
In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.
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Affiliation(s)
- Zihan Jin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, China
| | - Zihao Wei
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, China
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Das K, Paltani M, Tripathi PK, Kumar R, Verma S, Kumar S, Jain CK. Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:1286-1300. [PMID: 38213536 PMCID: PMC10776591 DOI: 10.37349/etat.2023.00197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 01/13/2024] Open
Abstract
Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.
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Affiliation(s)
- Kriti Das
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Maanvi Paltani
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Pankaj Kumar Tripathi
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
| | - Rajnish Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Saniya Verma
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Subodh Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Chakresh Kumar Jain
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
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Libouban PY, Aci-Sèche S, Gómez-Tamayo JC, Tresadern G, Bonnet P. The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks. Int J Mol Sci 2023; 24:16120. [PMID: 38003312 PMCID: PMC10671244 DOI: 10.3390/ijms242216120] [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: 09/14/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023] Open
Abstract
Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein-ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models' decision-making processes and accurately compare the performance of models.
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Affiliation(s)
- Pierre-Yves Libouban
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
| | - Samia Aci-Sèche
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
| | - Jose Carlos Gómez-Tamayo
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., B-2340 Beerse, Belgium; (J.C.G.-T.); (G.T.)
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., B-2340 Beerse, Belgium; (J.C.G.-T.); (G.T.)
| | - Pascal Bonnet
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
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Tanabe M, Sakate R, Nakabayashi J, Tsumura K, Ohira S, Iwato K, Kimura T. A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases. Sci Rep 2023; 13:19358. [PMID: 37938624 PMCID: PMC10632405 DOI: 10.1038/s41598-023-46648-1] [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: 06/16/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023] Open
Abstract
In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development.
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Affiliation(s)
- Mao Tanabe
- Laboratory of Rare Disease Information and Resource Library, Center for Intractable Diseases and ImmunoGenomics Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan
| | - Ryuichi Sakate
- Laboratory of Rare Disease Information and Resource Library, Center for Intractable Diseases and ImmunoGenomics Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan
| | - Jun Nakabayashi
- Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minami-ashigara, Kanagawa, Japan
| | - Kyosuke Tsumura
- Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minami-ashigara, Kanagawa, Japan
| | - Shino Ohira
- Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minami-ashigara, Kanagawa, Japan
| | - Kaoru Iwato
- Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minami-ashigara, Kanagawa, Japan
| | - Tomonori Kimura
- Reverse Translational Research Project, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki-City, Osaka, Japan.
- KAGAMI Project, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan.
- Department of Nephrology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
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11
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Kim MJ, Kulkarni V, Goode MA, Sivesind TE. Exploring the interactions of antihistamine with retinoic acid receptor beta (RARB) by molecular dynamics simulations and genome-wide meta-analysis. J Mol Graph Model 2023; 124:108539. [PMID: 37331258 PMCID: PMC10529808 DOI: 10.1016/j.jmgm.2023.108539] [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: 02/23/2023] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 06/20/2023]
Abstract
Kaposi sarcoma (KS) is one of the most common AIDS-related malignant neoplasms, which can leave lesions on the skin among HIV patients. These lesions can be treated with 9-cis-retinoic acid (9-cis-RA), an endogenous ligand of retinoic acid receptors that has been FDA-approved for treatment of KS. However, topical application of 9-cis-RA can induce several unpleasant side effects, like headache, hyperlipidemia, and nausea. Hence, alternative therapeutics with less side effects are desirable. There are case reports associating over-the-counter antihistamine usage with regression of KS. Antihistamines competitively bind to H1 receptor and block the action of histamine, best known for being released in response to allergens. Furthermore, there are already dozens of antihistamines that are FDA-approved with less side effects than 9-cis-RA. This led our team to conduct a series of in-silico assays to determine whether antihistamines can activate retinoic acid receptors. First, we utilized high-throughput virtual screening and molecular dynamics simulations to model high-affinity interactions between antihistamines and retinoic acid receptor beta (RARβ). We then performed systems genetics analysis to identify a genetic association between H1 receptor itself and molecular pathways involved in KS. Together, these findings advocate for exploration of antihistamines against KS, starting with our two promising hit compounds, bepotastine and hydroxyzine, for experimental validation study in the future.
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Affiliation(s)
- Minjae J Kim
- University of Tennessee Health Sciences Center School of Medicine, Memphis, TN, USA.
| | | | - Micah A Goode
- University of Tennessee Health Sciences Center School of Medicine, Memphis, TN, USA.
| | - Torunn E Sivesind
- Department of Dermatology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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12
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Tran-Nguyen VK, Junaid M, Simeon S, Ballester PJ. A practical guide to machine-learning scoring for structure-based virtual screening. Nat Protoc 2023; 18:3460-3511. [PMID: 37845361 DOI: 10.1038/s41596-023-00885-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/03/2023] [Indexed: 10/18/2023]
Abstract
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.
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Affiliation(s)
| | - Muhammad Junaid
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Saw Simeon
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
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13
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Shen C, Zhang X, Hsieh CY, Deng Y, Wang D, Xu L, Wu J, Li D, Kang Y, Hou T, Pan P. A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers. Chem Sci 2023; 14:8129-8146. [PMID: 37538816 PMCID: PMC10395315 DOI: 10.1039/d3sc02044d] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/03/2023] [Indexed: 08/05/2023] Open
Abstract
Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions are only applicable to a specific task, e.g., binding affinity prediction, binding pose prediction or virtual screening, suggesting that the development of a scoring function with balanced performance in all critical tasks remains a grand challenge. To this end, we propose a novel parameterization strategy by introducing an adjustable binding affinity term that represents the correlation between the predicted outcomes and experimental data into the training of mixture density network. The resulting residue-atom distance likelihood potential not only retains the superior docking and screening power over all the other state-of-the-art approaches, but also achieves a remarkable improvement in scoring and ranking performance. We emphatically explore the impacts of several key elements on prediction accuracy as well as the task preference, and demonstrate that the performance of scoring/ranking and docking/screening tasks of a certain model could be well balanced through an appropriate manner. Overall, our study highlights the potential utility of our innovative parameterization strategy as well as the resulting scoring framework in future structure-based drug design.
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Affiliation(s)
- Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
- State Key Lab of CAD&CG, Zhejiang University Hangzhou 310058 Zhejiang China
- School of Public Health, Zhejiang University Hangzhou 310058 Zhejiang China
- 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
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology Changzhou 213001 China
| | - Jian Wu
- School of Public Health, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dan Li
- 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
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
- State Key Lab of CAD&CG, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
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14
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Aci-Sèche S, Bourg S, Bonnet P, Rebehmed J, de Brevern AG, Diharce J. A perspective on the sharing of docking data. Data Brief 2023; 49:109386. [PMID: 37492229 PMCID: PMC10365938 DOI: 10.1016/j.dib.2023.109386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/17/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023] Open
Abstract
Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.
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Affiliation(s)
- Samia Aci-Sèche
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Stéphane Bourg
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Pascal Bonnet
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Joseph Rebehmed
- Department of Computer Science and Mathematics, Lebanese, American University, Beirut, Lebanon
| | - Alexandre G. de Brevern
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, Biologie Intégrée du Globule Rouge, UMR_S 1134, DSIMB Bioinformatics team, 75014 Paris, France
| | - Julien Diharce
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, Biologie Intégrée du Globule Rouge, UMR_S 1134, DSIMB Bioinformatics team, 75014 Paris, France
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15
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Azad I, Khan T, Ahmad N, Khan AR, Akhter Y. Updates on drug designing approach through computational strategies: a review. Future Sci OA 2023; 9:FSO862. [PMID: 37180609 PMCID: PMC10167725 DOI: 10.2144/fsoa-2022-0085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.
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Affiliation(s)
- Iqbal Azad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Tahmeena Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Naseem Ahmad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Abdul Rahman Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, UP, 2260025, India
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16
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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17
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Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
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Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
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18
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Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, Lyngdoh NM, Das D, Bidarolli M, Sony HT. Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development. Int J Mol Sci 2023; 24:ijms24032026. [PMID: 36768346 PMCID: PMC9916967 DOI: 10.3390/ijms24032026] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/22/2023] Open
Abstract
The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.
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Affiliation(s)
- Chayna Sarkar
- Department of Pharmacology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Biswadeep Das
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
- Correspondence: ; Tel./Fax: +91-135-708-856-0009
| | - Vikram Singh Rawat
- Department of Psychiatry, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
| | - Julie Birdie Wahlang
- Department of Pharmacology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Arvind Nongpiur
- Department of Psychiatry, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Iadarilang Tiewsoh
- Department of Medicine, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Nari M. Lyngdoh
- Department of Anesthesiology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Debasmita Das
- Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore 632014, Tamil Nadu, India
| | - Manjunath Bidarolli
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
| | - Hannah Theresa Sony
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
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19
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Trisciuzzi D, Siragusa L, Baroni M, Cruciani G, Nicolotti O. An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening. J Chem Inf Model 2022; 62:6812-6824. [PMID: 36320100 DOI: 10.1021/acs.jcim.2c00583] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.
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Affiliation(s)
- Daniela Trisciuzzi
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Lydia Siragusa
- Molecular Horizon s.r.l., Via Montelino, 30, 06084Bettona (PG), Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Massimo Baroni
- Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, Università degli Studi di Perugia, via Elce di Sotto, 8, 06123Perugia (PG), Italy
| | - Orazio Nicolotti
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy
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20
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Qu X, Dong L, Zhang J, Si Y, Wang B. Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules. J Chem Inf Model 2022; 62:4369-4379. [PMID: 36083808 DOI: 10.1021/acs.jcim.2c00916] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein-ligand interfaces, are expected to improve the prediction performance for diverse SFs.
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Affiliation(s)
- Xiaoyang Qu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Jinyan Zhang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Yubing Si
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
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21
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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22
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Shen C, Zhang X, Deng Y, Gao J, Wang D, Xu L, Pan P, Hou T, Kang Y. Boosting Protein-Ligand Binding Pose Prediction and Virtual Screening Based on Residue-Atom Distance Likelihood Potential and Graph Transformer. J Med Chem 2022; 65:10691-10706. [PMID: 35917397 DOI: 10.1021/acs.jmedchem.2c00991] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The past few years have witnessed enormous progress toward applying machine learning approaches to the development of protein-ligand scoring functions. However, the robust performance and wide applicability of scoring functions remain a big challenge for increasing the success rate of docking-based virtual screening. Herein, a novel scoring function named RTMScore was developed by introducing a tailored residue-based graph representation strategy and several graph transformer layers for the learning of protein and ligand representations, followed by a mixture density network to obtain residue-atom distance likelihood potential. Our approach was resolutely validated on the CASF-2016 benchmark, and the results indicate that RTMScore can outperform almost all of the other state-of-the-art methods in terms of both the docking and screening powers. Further evaluation confirms the robustness of our approach that can not only retain its docking power on cross-docked poses but also achieve improved performance as a rescoring tool in larger-scale virtual screening.
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Affiliation(s)
- Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China.,CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Junbo Gao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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23
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Yang C, Chen EA, Zhang Y. Protein-Ligand Docking in the Machine-Learning Era. Molecules 2022; 27:4568. [PMID: 35889440 PMCID: PMC9323102 DOI: 10.3390/molecules27144568] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/14/2022] [Indexed: 11/16/2022] Open
Abstract
Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.
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Affiliation(s)
- Chao Yang
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
| | - Eric Anthony Chen
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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24
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Conev A, Devaurs D, Rigo MM, Antunes DA, Kavraki LE. 3pHLA-score improves structure-based peptide-HLA binding affinity prediction. Sci Rep 2022; 12:10749. [PMID: 35750701 PMCID: PMC9232595 DOI: 10.1038/s41598-022-14526-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/08/2022] [Indexed: 12/30/2022] Open
Abstract
Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta's ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines.
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Affiliation(s)
- Anja Conev
- grid.21940.3e0000 0004 1936 8278Department of Computer Science, Rice University, Houston, 77005 USA
| | - Didier Devaurs
- grid.4305.20000 0004 1936 7988MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU UK
| | - Mauricio Menegatti Rigo
- grid.21940.3e0000 0004 1936 8278Department of Computer Science, Rice University, Houston, 77005 USA
| | | | - Lydia E. Kavraki
- grid.21940.3e0000 0004 1936 8278Department of Computer Science, Rice University, Houston, 77005 USA
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25
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Nag S, Baidya ATK, Mandal A, Mathew AT, Das B, Devi B, Kumar R. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022; 12:110. [PMID: 35433167 PMCID: PMC8994527 DOI: 10.1007/s13205-022-03165-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 03/18/2022] [Indexed: 12/26/2022] Open
Abstract
A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.
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Affiliation(s)
- Sagorika Nag
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Anurag T. K. Baidya
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Abhimanyu Mandal
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Alen T. Mathew
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Bhanuranjan Das
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Bharti Devi
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Rajnish Kumar
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
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26
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Gomes IDS, Santana CA, Marcolino LS, de Lima LHF, de Melo-Minardi RC, Dias RS, de Paula SO, Silveira SDA. Computational prediction of potential inhibitors for SARS-COV-2 main protease based on machine learning, docking, MM-PBSA calculations, and metadynamics. PLoS One 2022; 17:e0267471. [PMID: 35452494 PMCID: PMC9032443 DOI: 10.1371/journal.pone.0267471] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 04/06/2022] [Indexed: 11/23/2022] Open
Abstract
The development of new drugs is a very complex and time-consuming process, and for this reason, researchers have been resorting heavily to drug repurposing techniques as an alternative for the treatment of various diseases. This approach is especially interesting when it comes to emerging diseases with high rates of infection, because the lack of a quickly cure brings many human losses until the mitigation of the epidemic, as is the case of COVID-19. In this work, we combine an in-house developed machine learning strategy with docking, MM-PBSA calculations, and metadynamics to detect potential inhibitors for SARS-COV-2 main protease among FDA approved compounds. To assess the ability of our machine learning strategy to retrieve potential compounds we calculated the Enrichment Factor of compound datasets for three well known protein targets: HIV-1 reverse transcriptase (PDB 4B3P), 5-HT2A serotonin receptor (PDB 6A94), and H1 histamine receptor (PDB 3RZE). The Enrichment Factor for each target was, respectively, 102.5, 12.4, 10.6, which are considered significant values. Regarding the identification of molecules that can potentially inhibit the main protease of SARS-COV-2, compounds output by the machine learning step went through a docking experiment against SARS-COV-2 Mpro. The best scored poses were the input for MM-PBSA calculations and metadynamics using CHARMM and AMBER force fields to predict the binding energy for each complex. Our work points out six molecules, highlighting the strong interaction obtained for Mpro-mirabegron complex. Among these six, to the best of our knowledge, ambenonium has not yet been described in the literature as a candidate inhibitor for the SARS-COV-2 main protease in its active pocket.
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Affiliation(s)
- Isabela de Souza Gomes
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Charles Abreu Santana
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Leonardo Henrique França de Lima
- Department of Exact and Biological Sciences, Universidade Federal de São João del-Rei, Sete Lagoas Campus, Sete Lagoas, Minas Gerais, Brazil
| | - Raquel Cardoso de Melo-Minardi
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Roberto Sousa Dias
- Department of General Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
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27
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Shimazaki T, Tachikawa M. Collaborative Approach between Explainable Artificial Intelligence and Simplified Chemical Interactions to Explore Active Ligands for Cyclin-Dependent Kinase 2. ACS OMEGA 2022; 7:10372-10381. [PMID: 35382271 PMCID: PMC8973106 DOI: 10.1021/acsomega.1c06976] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/09/2022] [Indexed: 05/13/2023]
Abstract
To improve virtual screening for drug discovery, we present a collaborative approach between explainable artificial intelligence (AI) and simplified chemical interaction scores to efficiently search for active ligands bound to the target receptor. In particular, we focus on cyclin-dependent kinase 2 (CDK2), which is well known as a cancer target protein. Docking simulation alone is insufficient to distinguish active ligands from decoy molecules. To identify active ligands, in this paper, machine learning is employed together with scoring functions that simplify the screened Coulomb and Lennard-Jones interactions between the ligands and residues of the target receptor. We demonstrate that these simplified interaction scores can significantly improve the classification ability of machine learning models. We also demonstrate that explainable AI together with the simplified scoring method can highlight the important residues of CDK2 for recognizing active ligands.
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Affiliation(s)
- Tomomi Shimazaki
- Graduate
School of Nanobioscience, Yokohama City
University, 22-2 Seto, Yokohama, Kanagawa 236-0027, Japan
| | - Masanori Tachikawa
- Graduate
School of Data Science, Yokohama City University, 22-2, Seto, Yokohama, Kanagawa 236-0027, Japan
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28
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Kumar SP, Dixit NY, Patel CN, Rawal RM, Pandya HA. PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments. J Comput Chem 2022; 43:847-863. [PMID: 35301752 DOI: 10.1002/jcc.26840] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 02/14/2022] [Accepted: 02/26/2022] [Indexed: 11/09/2022]
Abstract
Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.
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Affiliation(s)
- Sivakumar Prasanth Kumar
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India.,Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Nandan Y Dixit
- Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Chirag N Patel
- Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Rakesh M Rawal
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Himanshu A Pandya
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India.,Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
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29
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Oguike OE, Ugwuishiwu CH, Asogwa CN, Nnadi CO, Obonga WO, Attama AA. Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum. Mol Divers 2022; 26:3447-3462. [PMID: 35064444 PMCID: PMC8782692 DOI: 10.1007/s11030-022-10380-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/07/2022] [Indexed: 11/29/2022]
Abstract
Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to develop new and high potent drugs against Plasmodium falciparum. Some major challenges include the dearth of suitable animal models for anti-P. falciparum assays, resistance to first-line drugs, lack of vaccines and the complex life cycle of Plasmodium. Gladly, newer approaches to antimalarial drug discovery have emerged due to the release of large datasets by pharmaceutical companies. This review provides insights into these new approaches to drug discovery covering different machine learning tools, which enhance the development of new compounds. It provides a systematic review on the use and prospects of machine learning in predicting, classifying and clustering IC50 values of bioactive compounds against P. falciparum. The authors identified many machine learning tools yet to be applied for this purpose. However, Random Forest and Support Vector Machines have been extensively applied though on a limited dataset of compounds.
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Affiliation(s)
- Osondu Everestus Oguike
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Department of Computer Science, Faculty of Physical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
| | - Chikodili Helen Ugwuishiwu
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Department of Computer Science, Faculty of Physical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
| | - Caroline Ngozi Asogwa
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Department of Computer Science, Faculty of Physical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
| | - Charles Okeke Nnadi
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria. .,Deprtment of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.
| | - Wilfred Ofem Obonga
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Deprtment of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
| | - Anthony Amaechi Attama
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
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30
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Tran-Nguyen VK, Simeon S, Junaid M, Ballester PJ. Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions. Curr Res Struct Biol 2022; 4:206-210. [PMID: 35769111 PMCID: PMC9234010 DOI: 10.1016/j.crstbi.2022.06.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/14/2022] [Accepted: 06/02/2022] [Indexed: 10/31/2022] Open
Abstract
The interaction between PD1 and its ligand PDL1 has been shown to render tumor cells resistant to apoptosis and promote tumor progression. An innovative mechanism to inhibit the PD1/PDL1 interaction is PDL1 dimerization induced by small-molecule PDL1 binders. Structure-based virtual screening is a promising approach to discovering such small-molecule PD1/PDL1 inhibitors. Here we investigate which type of generic scoring functions is most suitable to tackle this problem. We consider CNN-Score, an ensemble of convolutional neural networks, as the representative of machine-learning scoring functions. We also evaluate Smina, a commonly used classical scoring function, and IFP, a top structural fingerprint similarity scoring function. These three types of scoring functions were evaluated on two test sets sharing the same set of small-molecule PD1/PDL1 inhibitors, but using different types of inactives: either true inactives (molecules with no in vitro PD1/PDL1 inhibition activity) or assumed inactives (property-matched decoy molecules generated from each active). On both test sets, CNN-Score performed much better than Smina, which in turn strongly outperformed IFP. The fact that the latter was the case, despite precluding any possibility of exploiting decoy bias, demonstrates the predictive value of CNN-Score for PDL1. These results suggest that re-scoring Smina-docked molecules with CNN-Score is a promising structure-based virtual screening method to discover new small-molecule inhibitors of this therapeutic target.
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31
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Wang DD, Chan MT, Yan H. Structure-based protein-ligand interaction fingerprints for binding affinity prediction. Comput Struct Biotechnol J 2021; 19:6291-6300. [PMID: 34900139 PMCID: PMC8637032 DOI: 10.1016/j.csbj.2021.11.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/09/2021] [Accepted: 11/13/2021] [Indexed: 11/17/2022] Open
Abstract
Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.
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Affiliation(s)
- Debby D Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China
| | - Moon-Tong Chan
- School of Science and Technology, Hong Kong Metropolitan University, 30 Good Shepherd St, Ho Man Tin, Hong Kong
| | - Hong Yan
- Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
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32
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Castro LHE, Sant'Anna CMR. Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications. Curr Top Med Chem 2021; 22:333-346. [PMID: 34844540 DOI: 10.2174/1568026621666211129140958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/23/2021] [Accepted: 10/28/2021] [Indexed: 11/22/2022]
Abstract
Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.
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Affiliation(s)
| | - Carlos Mauricio R Sant'Anna
- Programa de Pós-Graduação em Química, Instituto de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica. Brazil
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33
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Erikawa D, Yasuo N, Sekijima M. MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning. J Cheminform 2021; 13:94. [PMID: 34838134 PMCID: PMC8626955 DOI: 10.1186/s13321-021-00572-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 11/15/2021] [Indexed: 12/05/2022] Open
Abstract
The hit-to-lead process makes the physicochemical properties of the hit molecules that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process. The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process. In this study, we have developed a SMILES-based generative model that can be generated starting from a certain molecule. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network. We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization. The source code is available at https://github.com/sekijima-lab/mermaid.
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Affiliation(s)
- Daiki Erikawa
- Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, Yokohama, Japan
| | - Nobuaki Yasuo
- Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, S6-23, 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan
| | - Masakazu Sekijima
- Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, Yokohama, Japan. .,Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, S6-23, 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan.
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34
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Asai A, Konno M, Taniguchi M, Vecchione A, Ishii H. Computational healthcare: Present and future perspectives (Review). Exp Ther Med 2021; 22:1351. [PMID: 34659497 PMCID: PMC8515560 DOI: 10.3892/etm.2021.10786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 07/19/2021] [Indexed: 12/05/2022] Open
Abstract
Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare.
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Affiliation(s)
- Ayumu Asai
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan.,Artificial Intelligence Research Center, Osaka University, Ibaraki, Osaka 567-0047, Japan.,The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka 567-0047, Japan
| | - Masamitsu Konno
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
| | - Masateru Taniguchi
- The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka 567-0047, Japan
| | - Andrea Vecchione
- Department of Clinical and Molecular Medicine, University of Rome 'Sapienza', Santo Andrea Hospital, I-1035-00189 Rome, Italy
| | - Hideshi Ishii
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
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35
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Jia Y, Cai S, Muhoza B, Qi B, Li Y. Advance in dietary polyphenols as dipeptidyl peptidase-IV inhibitors to alleviate type 2 diabetes mellitus: aspects from structure-activity relationship and characterization methods. Crit Rev Food Sci Nutr 2021:1-16. [PMID: 34652225 DOI: 10.1080/10408398.2021.1989659] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Dietary polyphenols with great antidiabetic effects are the most abundant components in edible products. Dietary polyphenols have attracted attention as dipeptidyl peptidase-IV (DPP-IV) inhibitors and indirectly improve insulin secretion. The DPP-IV inhibitory activities of dietary polyphenols depend on their structural diversity. Screening methods that can be used to rapidly and accurately identify potential polyphenol DPP-IV inhibitors are urgently needed. This review focuses on the relationship between the structures of dietary polyphenols and their DPP-IV inhibitory effects. Different characterization methods used for polyphenols as DPP-IV inhibitors have been summarized and compared. We conclude that the position and number of hydroxyl groups, methoxy groups, glycosylated groups, and the extent of conjugation influence the efficiency of inhibition of DPP-IV. Various combinations of methods, such as in-vitro enzymatic inhibition, ex-vivo/in-vivo enzymatic inhibition, cell-based in situ, and in-silico virtual screening, are used to evaluate the DPP-IV inhibitory effects of dietary polyphenols. Further investigations of polyphenol DPP-IV inhibitors will improve the bioaccessibility and bioavailability of these bioactive compounds. Exploration of (i) dietary polyphenols derived from multiple targets, that can prevent diabetes, and (ii) actual binding interactions via multispectral analysis, to understand the binding interactions in the complexes, is required.
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Affiliation(s)
- Yijia Jia
- College of Food Science, Northeast Agricultural University, Harbin, China
| | - Shengbao Cai
- Faculty of Agriculture and Food, Yunnan Institute of Food Safety, Kunming University of Science and Technology, Kunming, Yunnan Province, China
| | - Bertrand Muhoza
- College of Food Science, Northeast Agricultural University, Harbin, China
| | - Baokun Qi
- College of Food Science, Northeast Agricultural University, Harbin, China.,Heilongjiang Green Food Science Research Institute, Harbin, China.,National Research Center of Soybean Engineering and Technology, Harbin, China
| | - Yang Li
- College of Food Science, Northeast Agricultural University, Harbin, China.,Heilongjiang Green Food Science Research Institute, Harbin, China.,National Research Center of Soybean Engineering and Technology, Harbin, China
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Crampon K, Giorkallos A, Deldossi M, Baud S, Steffenel LA. Machine-learning methods for ligand-protein molecular docking. Drug Discov Today 2021; 27:151-164. [PMID: 34560276 DOI: 10.1016/j.drudis.2021.09.007] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/14/2021] [Accepted: 09/15/2021] [Indexed: 12/22/2022]
Abstract
Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains use AI, including molecular simulation for drug discovery. In this review, we provide an overview of ligand-protein molecular docking and how machine learning (ML), especially deep learning (DL), a subset of ML, is transforming the field by tackling the associated challenges.
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Affiliation(s)
- Kevin Crampon
- Université de Reims Champagne Ardenne, CNRS, MEDyC UMR 7369, 51097 Reims, France; Université de Reims Champagne Ardenne, LICIIS - LRC CEA DIGIT, 51100 Reims, France; Atos SE, Center of Excellence in Advanced Computing, 38130 Echirolles, France
| | - Alexis Giorkallos
- Atos SE, Center of Excellence in Advanced Computing, 38130 Echirolles, France
| | - Myrtille Deldossi
- Atos SE, Center of Excellence in Advanced Computing, 38130 Echirolles, France
| | - Stéphanie Baud
- Université de Reims Champagne Ardenne, CNRS, MEDyC UMR 7369, 51097 Reims, France
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Di Filippo JI, Cavasotto CN. Guided structure-based ligand identification and design via artificial intelligence modeling. Expert Opin Drug Discov 2021; 17:71-78. [PMID: 34544293 DOI: 10.1080/17460441.2021.1979514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION The implementation of Artificial Intelligence (AI) methodologies to drug discovery (DD) are on the rise. Several applications have been developed for structure-based DD, where AI methods provide an alternative framework for the identification of ligands for validated therapeutic targets, as well as the de novo design of ligands through generative models. AREAS COVERED Herein, the authors review the contributions between the 2019 to present period regarding the application of AI methods to structure-based virtual screening (SBVS) which encompasses mainly molecular docking applications - binding pose prediction and binary classification for ligand or hit identification-, as well as de novo drug design driven by machine learning (ML) generative models, and the validation of AI models in structure-based screening. Studies are reviewed in terms of their main objective, used databases, implemented methodology, input and output, and key results . EXPERT OPINION More profound analyses regarding the validity and applicability of AI methods in DD have begun to appear. In the near future, we expect to see more structure-based generative models- which are scarce in comparison to ligand-based generative models-, the implementation of standard guidelines for validating the generated structures, and more analyses regarding the validation of AI methods in structure-based DD.
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Affiliation(s)
- Juan I Di Filippo
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina.,Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina.,Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
| | - Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina.,Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina.,Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
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Xiong G, Shen C, Yang Z, Jiang D, Liu S, Lu A, Chen X, Hou T, Cao D. Featurization strategies for protein–ligand interactions and their applications in scoring function development. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1567] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Guoli Xiong
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
| | - Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences Zhejiang University Hangzhou China
| | - Ziyi Yang
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
| | - Dejun Jiang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences Zhejiang University Hangzhou China
- College of Computer Science and Technology Zhejiang University Hangzhou China
| | - Shao Liu
- Department of Pharmacy Xiangya Hospital, Central South University Changsha China
| | - Aiping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis Xiangya Hospital, Central South University Changsha China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences Zhejiang University Hangzhou China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China
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Llanos MA, Gantner ME, Rodriguez S, Alberca LN, Bellera CL, Talevi A, Gavernet L. Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: the Main Protease (Mpro) Case Study. J Chem Inf Model 2021; 61:3758-3770. [PMID: 34313128 DOI: 10.1021/acs.jcim.1c00404] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.
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Affiliation(s)
- Manuel A Llanos
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Melisa E Gantner
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Santiago Rodriguez
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Lucas N Alberca
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Carolina L Bellera
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Luciana Gavernet
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
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40
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Kingdon ADH, Alderwick LJ. Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis. Comput Struct Biotechnol J 2021; 19:3708-3719. [PMID: 34285773 PMCID: PMC8258792 DOI: 10.1016/j.csbj.2021.06.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/22/2021] [Accepted: 06/22/2021] [Indexed: 12/12/2022] Open
Abstract
Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.
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Key Words
- CV, collective variable
- Docking
- Drug discovery
- In silico
- LIE, Linear Interaction Energy
- MD, Molecular Dynamic
- MDR, multi-drug resistant
- MMPB(GB)SA, Molecular Mechanics with Poisson Boltzmann (or generalised Born) and Surface Area solvation
- Machine learning
- Mt, Mycobacterium tuberculosis
- Mycobacterium tuberculosis
- PTC, peptidyl transferase centre
- RMSD, root-mean square-deviation
- Tuberculosis, TB
- cMD, Classical Molecular Dynamic
- cryo-EM, cryogenic electron microscopy
- ns, nanosecond
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Affiliation(s)
- Alexander D H Kingdon
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Luke J Alderwick
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
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Nigam A, Pollice R, Hurley MFD, Hickman RJ, Aldeghi M, Yoshikawa N, Chithrananda S, Voelz VA, Aspuru-Guzik A. Assigning confidence to molecular property prediction. Expert Opin Drug Discov 2021; 16:1009-1023. [DOI: 10.1080/17460441.2021.1925247] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- AkshatKumar Nigam
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Robert Pollice
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | | | - Riley J. Hickman
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Matteo Aldeghi
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, University Ave Suite 710, Toronto, Canada
| | - Naruki Yoshikawa
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | | | | | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, University Ave Suite 710, Toronto, Canada
- Canadian Institute for Advanced Research (CIFAR), University Ave, Toronto, Canada
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42
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Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16:1175-1192. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, U.S.A
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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43
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Suh D, Lee JW, Choi S, Lee Y. Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction. Int J Mol Sci 2021; 22:6032. [PMID: 34199677 PMCID: PMC8199773 DOI: 10.3390/ijms22116032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 05/29/2021] [Accepted: 05/29/2021] [Indexed: 01/23/2023] Open
Abstract
The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.
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Affiliation(s)
- Donghyuk Suh
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Jai Woo Lee
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Sun Choi
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Korea
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Artificial intelligence in drug design: algorithms, applications, challenges and ethics. FUTURE DRUG DISCOVERY 2021. [DOI: 10.4155/fdd-2020-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.
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Ghislat G, Rahman T, Ballester PJ. Recent progress on the prospective application of machine learning to structure-based virtual screening. Curr Opin Chem Biol 2021; 65:28-34. [PMID: 34052776 DOI: 10.1016/j.cbpa.2021.04.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/13/2021] [Accepted: 04/23/2021] [Indexed: 12/30/2022]
Abstract
As more bioactivity and protein structure data become available, scoring functions (SFs) using machine learning (ML) to leverage these data sets continue to gain further accuracy and broader applicability. Advances in our understanding of the optimal ways to train and evaluate these ML-based SFs have introduced further improvements. One of these advances is how to select the most suitable decoys (molecules assumed inactive) to train or test an ML-based SF on a given target. We also review the latest applications of ML-based SFs for prospective structure-based virtual screening (SBVS), with a focus on the observed improvement over those using classical SFs. Finally, we provide recommendations for future prospective SBVS studies based on the findings of recent methodological studies.
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Affiliation(s)
- Ghita Ghislat
- U1104, CNRS UMR7280, Centre D'Immunologie de Marseille-Luminy, Inserm, Marseille, France
| | - Taufiq Rahman
- Department of Pharmacology, University of Cambridge, Cambridge, CB2 1PD, UK
| | - Pedro J Ballester
- Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, Marseille, F-13009, France; CNRS, UMR7258, Marseille, F-13009, France; Institut Paoli-Calmettes, Marseille, F-13009, France; Aix-Marseille University, UM 105, F-13284, Marseille, France.
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Bitencourt-Ferreira G, Rizzotto C, de Azevedo Junior WF. Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS. Curr Med Chem 2021; 28:1746-1756. [PMID: 32410551 DOI: 10.2174/0929867327666200515101820] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/06/2020] [Accepted: 04/07/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. OBJECTIVE Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. METHODS SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions. RESULTS Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. CONCLUSION Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.
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Affiliation(s)
| | - Camila Rizzotto
- Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre-RS, Brazil
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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Yasuo N, Ishida T, Sekijima M. Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project. Parasitol Int 2021; 83:102366. [PMID: 33915269 DOI: 10.1016/j.parint.2021.102366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/23/2021] [Accepted: 04/07/2021] [Indexed: 01/09/2023]
Abstract
Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.
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Affiliation(s)
- Nobuaki Yasuo
- Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, S6-23, 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan.
| | - Takashi Ishida
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, W8-85, 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan.
| | - Masakazu Sekijima
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Japan.
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Abstract
The recent outbreak of the highly contagious coronavirus disease 2019 (COVID-19) caused by the novel coronavirus SARS-CoV-2 has created a global health crisis with socioeconomic impacts. Although, recently, vaccines have been approved for the prevention of COVID-19, there is still an urgent need for the discovery of more efficacious and safer drugs especially from natural sources. In this study, a number of quinoline and quinazoline alkaloids with antiviral and/or antimalarial activity were virtually screened against three potential targets for the development of drugs against COVID-19. Among seventy-one tested compounds, twenty-three were selected for molecular docking based on their pharmacokinetic and toxicity profiles. The results identified a number of potential inhibitors. Three of them, namely, norquinadoline A, deoxytryptoquivaline, and deoxynortryptoquivaline, showed strong binding to the three targets, SARS-CoV-2 main protease, spike glycoprotein, and human angiotensin-converting enzyme 2. These alkaloids therefore have promise for being further investigated as possible multitarget drugs against COVID-19.
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Bitencourt-Ferreira G, Duarte da Silva A, Filgueira de Azevedo W. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2. Curr Med Chem 2021; 28:253-265. [PMID: 31729287 DOI: 10.2174/2213275912666191102162959] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 08/22/2019] [Accepted: 09/24/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. OBJECTIVE Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. METHODS We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. RESULTS Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. CONCLUSION Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.
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Affiliation(s)
- Gabriela Bitencourt-Ferreira
- Laboratory of Computational Systems Biology. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900 , Brazil
| | - Amauri Duarte da Silva
- Specialization Program in Bioinformatics. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900, Brazil
| | - Walter Filgueira de Azevedo
- Laboratory of Computational Systems Biology. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900 , Brazil
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