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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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Yang J, Cai Y, Zhao K, Xie H, Chen X. Concepts and applications of chemical fingerprint for hit and lead screening. Drug Discov Today 2022; 27:103356. [PMID: 36113834 DOI: 10.1016/j.drudis.2022.103356] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 07/28/2022] [Accepted: 09/08/2022] [Indexed: 11/22/2022]
Abstract
Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.
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Affiliation(s)
- Jingbo Yang
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China
| | - Yiyang Cai
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China
| | - Kairui Zhao
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China
| | - Hongbo Xie
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China.
| | - Xiujie Chen
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China.
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Abstract
Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large library of compounds, including approximately 12 000 environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects. The Tox21 Data Challenge offered a platform to evaluate different computational methods for toxicity predictions. Inspired by the success of multiscale weighted colored graph (MWCG) theory in protein-ligand binding affinity predictions, we consider MWCG theory for toxicity analysis. In the present work, we develop a geometric graph learning toxicity (GGL-Tox) model by integrating MWCG features and the gradient boosting decision tree (GBDT) algorithm. The benchmark tests of the Tox21 Data Challenge are employed to demonstrate the utility and usefulness of the proposed GGL-Tox model. An extensive comparison with other state-of-the-art models indicates that GGL-Tox is an accurate and efficient model for toxicity analysis and prediction.
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Affiliation(s)
- Jian Jiang
- Research Center of Nonlinear Science, College of Mathematics and Computer Science, Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, Wuhan 430200, P R. China
| | - Rui Wang
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
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Bondarev NV. Artificial Neural Network and Multiple Linear Regression for Prediction and Classification of Sustainability of Sodium and Potassium Coronates. RUSS J GEN CHEM+ 2019. [DOI: 10.1134/s1070363219070144] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Cha HJ, Song YJ, Lee DE, Kim YH, Shin J, Jang CG, Suh SK, Kim SJ, Yun J. Receptor Binding Affinities of Synthetic Cannabinoids Determined by Non-Isotopic Receptor Binding Assay. Toxicol Res 2019; 35:37-44. [PMID: 30766656 PMCID: PMC6354946 DOI: 10.5487/tr.2019.35.1.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 08/07/2018] [Accepted: 08/21/2018] [Indexed: 01/24/2023] Open
Abstract
A major predictor of the efficacy of natural or synthetic cannabinoids is their binding affinity to the cannabinoid type I receptor (CB1) in the central nervous system, as the main psychological effects of cannabinoids are achieved via binding to this receptor. Conventionally, receptor binding assays have been performed using isotopes, which are inconvenient owing to the effects of radioactivity. In the present study, the binding affinities of five cannabinoids for purified CB1 were measured using a surface plasmon resonance (SPR) technique as a putative non-isotopic receptor binding assay. Results were compared with those of a radio-isotope-labeled receptor binding assay. The representative natural cannabinoid Δ9-tetrahydrocannabinol and four synthetic cannabinoids, JWH-015, JWH-210, RCS-4, and JWH-250, were assessed using both the SPR biosensor assay and the conventional isotopic receptor binding assay. The binding affinities of the test substances to CB1 were determined to be (from highest to lowest) 9.52 × 10-13 M (JWH-210), 6.54 × 10-12 M (JWH-250), 1.56 × 10-11 M (Δ9-tetrahydrocannabinol), 2.75 × 10-11 M (RCS-4), and 6.80 ×10-11 M (JWH-015) using the non-isotopic method. Using the conventional isotopic receptor binding assay, the same order of affinities was observed. In conclusion, our results support the use of kinetic analysis via SPR in place of the isotopic receptor binding assay. To replace the receptor binding affinity assay with SPR techniques in routine assays, further studies for method validation will be needed in the future.
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Affiliation(s)
- Hye Jin Cha
- Pharmacological Research Division, Toxicological Evaluation and Research Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Chungju, Korea
| | - Yun Jeong Song
- Pharmacological Research Division, Toxicological Evaluation and Research Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Chungju, Korea
| | - Da Eun Lee
- Pharmacological Research Division, Toxicological Evaluation and Research Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Chungju, Korea
| | - Young-Hoon Kim
- Pharmacological Research Division, Toxicological Evaluation and Research Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Chungju, Korea
| | - Jisoon Shin
- Pharmacological Research Division, Toxicological Evaluation and Research Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Chungju, Korea
| | - Choon-Gon Jang
- Department of Pharmacology, School of Pharmacy, Sungkyunkwan University, Suwon, Korea
| | - Soo Kyung Suh
- Pharmacological Research Division, Toxicological Evaluation and Research Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Chungju, Korea
| | - Sung Jin Kim
- Cosmetics Policy Division, Ministry of Food and Drug Safety, Chungju, Korea
| | - Jaesuk Yun
- Neuroimmunology Lab, College of Pharmacy, Wonkwang University, Iksan, Korea
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Abstract
Although significant advances in experimental high throughput screening (HTS) have been made for drug lead identification, in silico virtual screening (VS) is indispensable owing to its unique advantage over experimental HTS, target-focused, cheap, and efficient, albeit its disadvantage of producing false positive hits. For both experimental HTS and VS, the quality of screening libraries is crucial and determines the outcome of those studies. In this paper, we first reviewed the recent progress on screening library construction. We realized the urgent need for compiling high-quality screening libraries in drug discovery. Then we compiled a set of screening libraries from about 20 million druglike ZINC molecules by running fingerprint-based similarity searches against known drug molecules. Lastly, the screening libraries were objectively evaluated using 5847 external actives covering more than 2000 drug targets. The result of the assessment is very encouraging. For example, with the Tanimoto coefficient being set to 0.75, 36% of external actives were retrieved and the enrichment factor was 13. Additionally, drug target family specific screening libraries were also constructed and evaluated. The druglike screening libraries are available for download from https://mulan.pharmacy.pitt.edu .
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Affiliation(s)
- Junmei Wang
- Department of Pharmaceutical Sciences , The University of Pittsburgh , 3501 Terrace Street , Pittsburgh , Pennsylvania 15261 , United States
| | - Yubin Ge
- Department of Pharmaceutical Sciences , The University of Pittsburgh , 3501 Terrace Street , Pittsburgh , Pennsylvania 15261 , United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences , The University of Pittsburgh , 3501 Terrace Street , Pittsburgh , Pennsylvania 15261 , United States
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Xu Y, Yao H, Lin K. An overview of neural networks for drug discovery and the inputs used. Expert Opin Drug Discov 2018; 13:1091-1102. [DOI: 10.1080/17460441.2018.1547278] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Yinqiu Xu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Hequan Yao
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Kejiang Lin
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
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Wu Y, Wang G. Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis. Int J Mol Sci 2018; 19:E2358. [PMID: 30103448 PMCID: PMC6121588 DOI: 10.3390/ijms19082358] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 07/31/2018] [Accepted: 08/08/2018] [Indexed: 02/07/2023] Open
Abstract
Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.
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Affiliation(s)
- Yunyi Wu
- Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Southern University of Science and Technology, Shenzhen 518055, China.
| | - Guanyu Wang
- Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Southern University of Science and Technology, Shenzhen 518055, China.
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Jing Y, Bian Y, Hu Z, Wang L, Xie XQ. Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era. AAPS JOURNAL 2018; 20:58. [PMID: 29603063 DOI: 10.1208/s12248-018-0210-0] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Accepted: 02/22/2018] [Indexed: 12/22/2022]
Abstract
Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.
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Affiliation(s)
- Yankang Jing
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, Pennsylvania, 15261, USA.,NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA.,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA
| | - Yuemin Bian
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, Pennsylvania, 15261, USA.,NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA.,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA
| | - Ziheng Hu
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, Pennsylvania, 15261, USA.,NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA.,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA
| | - Lirong Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, Pennsylvania, 15261, USA.,NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA.,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, Pennsylvania, 15261, USA. .,NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA. .,Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA. .,Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA.
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Paulke A, Proschak E, Sommer K, Achenbach J, Wunder C, Toennes SW. Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model. Toxicol Lett 2016; 245:1-6. [DOI: 10.1016/j.toxlet.2016.01.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 12/27/2015] [Accepted: 01/08/2016] [Indexed: 11/15/2022]
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11
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Smusz S, Kurczab R, Satała G, Bojarski AJ. Fingerprint-based consensus virtual screening towards structurally new 5-HT6R ligands. Bioorg Med Chem Lett 2015; 25:1827-30. [DOI: 10.1016/j.bmcl.2015.03.049] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2015] [Revised: 03/18/2015] [Accepted: 03/19/2015] [Indexed: 01/31/2023]
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