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Wang NN, Zhu B, Li XL, Liu S, Shi JY, Cao DS. Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities. J Chem Inf Model 2024; 64:96-109. [PMID: 38132638 DOI: 10.1021/acs.jcim.3c01304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.
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Affiliation(s)
- Ning-Ning Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Bei Zhu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shanxi, P.R. China
| | - Xin-Liang Li
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shanxi, P.R. China
| | - Dong-Sheng Cao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P.R. China
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Wang Y, Xu J, Zhang J, Xu H, Sun Y, Miao Y, Wen T. SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances. BMC Med Inform Decis Mak 2023; 23:79. [PMID: 37143043 PMCID: PMC10157906 DOI: 10.1186/s12911-023-02141-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 03/10/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Clinical practices have demonstrated that disease treatment can be very complex. Patients with chronic diseases often suffer from more than one disease. Complex diseases are often treated with a variety of drugs, including both primary and auxiliary treatments. This complexity and multidimensionality increase the difficulty of extracting knowledge from clinical data. METHODS In this study, we proposed a subgroup identification algorithm for complex prescriptions (SIAP). We applied the SIAP algorithm to identify the importance level of each drug in complex prescriptions. The algorithm quickly classified and determined valid prescription combinations for patients. The algorithm was validated through classification matching of classical prescriptions in traditional Chinese medicine. We collected 376 formulas and their compositions from a formulary to construct a database of standard prescriptions. We also collected 1438 herbal prescriptions from clinical data for automated prescription identification. The prescriptions were divided into training and test sets. Finally, the parameters of the two sub-algorithms of SIAP and SIAP-All, as well as those of the combination algorithm SIAP + All, were optimized on the training set. A comparison analysis was performed against the baseline intersection set rate (ISR) algorithm. The algorithm for this study was implemented with Python 3.6. RESULTS The SIAP-All and SIAP + All algorithms outperformed the benchmark ISR algorithm in terms of accuracy, recall, and F1 value. The F1 values were 0.7568 for SIAP-All and 0.7799 for SIAP + All, showing improvements of 8.73% and 11.04% over the existing ISR algorithm, respectively. CONCLUSION We developed an algorithm, SIAP, to automatically match sub-prescriptions of complex drugs with corresponding standard or classic prescriptions. The matching algorithm weights the drugs in the prescription according to their importance level. The results of this study can help to classify and analyse the drug compositions of complex prescriptions.
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Affiliation(s)
- Yifei Wang
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China
| | - Julia Xu
- The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Jie Zhang
- Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100085, China
| | - Hong Xu
- College of Engineering and Science, Victoria University, Melbourne, VIC, 3000, Australia
| | - Yuzhong Sun
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Yuan Miao
- College of Engineering and Science, Victoria University, Melbourne, VIC, 3000, Australia.
| | - Tiancai Wen
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
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Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model. Pharmaceuticals (Basel) 2023. [DOI: 10.3390/ph16020250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
Abstract
As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R2 = 70.27, RN = −0.008, a(R2) = 0.014, and δK = 0.021) and its prediction ability (Q2LMO = 67.89, Q2EXT = 67.75, a(Q2) = −0.068, δQ = 0.0, rm2¯ = 0.229, and Δrm2 = 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules (R2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii.
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McNair D. Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond. Annu Rev Pharmacol Toxicol 2023; 63:77-97. [PMID: 35679624 DOI: 10.1146/annurev-pharmtox-051921-023255] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.
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Affiliation(s)
- Douglas McNair
- Global Health, Integrated Development, Bill & Melinda Gates Foundation, Seattle, Washington, USA;
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5
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Ren S, Yu L, Gao L. Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction. Bioinformatics 2022; 38:4387-4394. [PMID: 35904544 DOI: 10.1093/bioinformatics/btac538] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/06/2022] [Accepted: 07/27/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected pharmacological effects. Therefore, efficient identification of drug interactions is essential for the treatment of complex diseases. Currently proposed calculation methods are often limited by the collection of redundant drug features, a small amount of labeled data and low model generalization capabilities. Meanwhile, there is also a lack of unique methods for multidrug representation learning, which makes it more difficult to take full advantage of the originally scarce data. RESULTS Inspired by graph models and pretraining models, we integrated a large amount of unlabeled drug molecular graph information and target information, then designed a pretraining framework, MGP-DR (Molecular Graph Pretraining for Drug Representation), specifically for drug pair representation learning. The model uses self-supervised learning strategies to mine the contextual information within and between drug molecules to predict drug-drug interactions and drug combinations. The results achieved promising performance across multiple metrics compared with other state-of-the-art methods. Our MGP-DR model can be used to provide a reliable candidate set for the combined use of multiple drugs. AVAILABILITY AND IMPLEMENTATION Code of the model, datasets and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MGP-DR). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shujie Ren
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
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6
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Sun M, She S, Chen H, Cheng J, Ji W, Wang D, Feng C. Prediction Model for Synergistic Anti-tumor Multi-compound Combinations from Traditional Chinese Medicine based on Extreme Gradient Boosting, Targets and Gene Expression Data. J Bioinform Comput Biol 2022; 20:2250016. [DOI: 10.1142/s0219720022500160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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7
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CDCDB: A large and continuously updated drug combination database. Sci Data 2022; 9:263. [PMID: 35654801 PMCID: PMC9163158 DOI: 10.1038/s41597-022-01360-z] [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: 04/19/2021] [Accepted: 04/28/2022] [Indexed: 12/25/2022] Open
Abstract
In recent years, due to the complementary action of drug combinations over mono-therapy, the multiple-drugs for multiple-targets paradigm has received increased attention to treat bacterial infections and complex diseases. Although new drug combinations screening has benefited from experimental tests like automated high throughput screening, it is limited due to the large number of possible drug combinations. The task of drug combination screening can be streamlined through computational methods and models. Such models require up-to-date databases; however, existing databases are static and consist of the data collected at the time of their creation. This paper introduces the Continuous Drug Combination Database (CDCDB), a continuously updated drug combination database. The CDCDB includes over 40,795 drug combinations, of which 17,107 are unique combinations consisting of more than 4,129 individual drugs, curated from ClinicalTrials.gov, the FDA Orange Book®, and patents. To create CDCDB, we use various methods, including natural language processing techniques, to improve the process of drug combination discovery, ensuring that our database can be used for drug synergy prediction. Website: https://icc.ise.bgu.ac.il/medical_ai/CDCDB/. Measurement(s) | drug combination effect modeling • drug combination effect modeling | Technology Type(s) | Text mining • Clinical Trials Informatics System | Factor Type(s) | Medicine | Sample Characteristic - Organism | Homo sapiens |
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8
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Sinha K, Ghosh J, Sil PC. Machine Learning in Drug Metabolism Study. Curr Drug Metab 2022; 23:1012-1026. [PMID: 36578255 DOI: 10.2174/1389200224666221227094144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 12/30/2022]
Abstract
Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.
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Affiliation(s)
- Krishnendu Sinha
- Department of Zoology, Jhargram Raj College, Jhargram-721507, India
| | - Jyotirmoy Ghosh
- Department of Chemistry, Banwarilal Bhalotia College, Asansol-713303, India
| | - Parames Chandra Sil
- Department of Division of Molecular Medicine, Bose Institute, Kolkata-700054, India
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9
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Zhang Q, Liu P, Wang X, Zhang Y, Han Y, Yu B. StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106921] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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10
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Tong Z, Zhou Y, Wang J. Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine. Sci Rep 2019; 9:10442. [PMID: 31320657 PMCID: PMC6639372 DOI: 10.1038/s41598-019-46540-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 06/26/2019] [Indexed: 02/08/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one major cause of cancer-related death worldwide. But now, the systematic therapy for the advanced stages of HCC is rather limited. Thus, the discovery of novel drug targets and thereafter targeted drugs against HCC is continuously needed. In this study, we combined clinical association data, gene expression profiles and manually collected drug target genes with the human protein-protein interaction (PPI) network to establish an in-silico HCC drug target predictor. First, we found drug target genes (DTGs), disease-associated genes (DAGs), prognostic unfavorable genes (PUGs) and cancer up-regulated genes (URGs) have higher degree, betweenness, closeness centrality, while cancer down-regulated genes (DRGs), prognostic favorable genes (PFGs) have lower degrees, in comparison with background genes. Moreover, DTG nodes were shown to be closer to DAG, PUG and URG nodes, but farther away from PFG and DRG nodes. Compared to the background, PFGs and DRGs were shown to have relatively bigger genetic dependency scores, while PUGs and URGs have smaller genetic dependency scores. Finally, based on the observed features of DTGs, we constructed a drug target predictor using one-class support vector machine (one-class SVM). Performance evaluation results suggested our predictor could effectively identify putative drug target genes for further research.
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Affiliation(s)
- Zhan Tong
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China.
| | - Juan Wang
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China.
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11
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Yang S, Shen Y, Lu W, Yang Y, Wang H, Li L, Wu C, Du G. Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription. BIOMED RESEARCH INTERNATIONAL 2019; 2019:6847685. [PMID: 31360720 PMCID: PMC6652039 DOI: 10.1155/2019/6847685] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 05/13/2019] [Accepted: 05/26/2019] [Indexed: 12/18/2022]
Abstract
Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-induced and H2O2-induced brain cell damage. In this study, a phenotype-based classification method was designed by machine learning to identify neuroprotective compounds and to clarify the compatibility of XXMD components. Four different single classifiers (AB, kNN, CT, and RF) and molecular fingerprint descriptors were used to construct stacked naïve Bayesian models. Among them, the RF algorithm had a better performance with an average MCC value of 0.725±0.014 and 0.774±0.042 from 5-fold cross-validation and test set, respectively. The probability values calculated by four models were then integrated into a stacked Bayesian model. In total, two optimal models, s-NB-1-LPFP6 and s-NB-2-LPFP6, were obtained. The two validated optimal models revealed Matthews correlation coefficients (MCC) of 0.968 and 0.993 for 5-fold cross-validation and of 0.874 and 0.959 for the test set, respectively. Furthermore, the two models were used for virtual screening experiments to identify neuroprotective compounds in XXMD. Ten representative compounds with potential therapeutic effects against the two phenotypes were selected for further cell-based assays. Among the selected compounds, two compounds significantly inhibited H2O2-induced and Na2S2O4-induced neurotoxicity simultaneously. Together, our findings suggested that machine learning algorithms such as combination Bayesian models were feasible to predict neuroprotective compounds and to preliminarily demonstrate the pharmacological mechanisms of TCM.
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Affiliation(s)
- Shilun Yang
- School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103, Wen hua Road, Shenyang 110016, China
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Yanjia Shen
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Wendan Lu
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Yinglin Yang
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Haigang Wang
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Li Li
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Chunfu Wu
- School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103, Wen hua Road, Shenyang 110016, China
| | - Guanhua Du
- School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103, Wen hua Road, Shenyang 110016, China
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
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12
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Xiong Y, Qiao Y, Kihara D, Zhang HY, Zhu X, Wei DQ. Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates. Curr Drug Metab 2019; 20:229-235. [PMID: 30338736 DOI: 10.2174/1389200219666181019094526] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 08/05/2018] [Accepted: 08/06/2018] [Indexed: 12/23/2022]
Abstract
Background:Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism is time- and recourse- consuming. In the phase I metabolism of foreign compounds (i.e., most of drugs), cytochrome P450 enzymes play a key role. To help develop drugs with proper ADME properties, computational models are highly desired to predict the ADME properties of drug candidates, particularly for drugs binding to cytochrome P450.Objective:This narrative review aims to briefly summarize machine learning techniques used in the prediction of the cytochrome P450 isoform specificity of drug candidates.Results:Both single-label and multi-label classification methods have demonstrated good performance on modelling and prediction of the isoform specificity of substrates based on their quantitative descriptors.Conclusion:This review provides a guide for researchers to develop machine learning-based methods to predict the cytochrome P450 isoform specificity of drug candidates.
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Affiliation(s)
- Yi Xiong
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanhua Qiao
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN 47907, United States
| | - Hui-Yuan Zhang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaolei Zhu
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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13
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Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction. Int J Mol Sci 2019; 20:ijms20092175. [PMID: 31052500 PMCID: PMC6539940 DOI: 10.3390/ijms20092175] [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: 02/21/2019] [Revised: 04/14/2019] [Accepted: 04/29/2019] [Indexed: 01/11/2023] Open
Abstract
Biologically active chemical compounds may provide remedies for several diseases. Meanwhile, Machine Learning techniques applied to Drug Discovery, which are cheaper and faster than wet-lab experiments, have the capability to more effectively identify molecules with the expected pharmacological activity. Therefore, it is urgent and essential to develop more representative descriptors and reliable classification methods to accurately predict molecular activity. In this paper, we investigate the potential of a novel representation based on Spherical Harmonics fed into Probabilistic Classification Vector Machines classifier, namely SHPCVM, to compound the activity prediction task. We make use of representation learning to acquire the features which describe the molecules as precise as possible. To verify the performance of SHPCVM ten-fold cross-validation tests are performed on twenty-one G protein-coupled receptors (GPCRs). Experimental outcomes (accuracy of 0.86) assessed by the classification accuracy, precision, recall, Matthews’ Correlation Coefficient and Cohen’s kappa reveal that using our Spherical Harmonics-based representation which is relatively short and Probabilistic Classification Vector Machines can achieve very satisfactory performance results for GPCRs.
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14
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Vakil V, Trappe W. Drug Combinations: Mathematical Modeling and Networking Methods. Pharmaceutics 2019; 11:E208. [PMID: 31052580 PMCID: PMC6571786 DOI: 10.3390/pharmaceutics11050208] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/24/2019] [Accepted: 04/27/2019] [Indexed: 12/14/2022] Open
Abstract
Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. A description of existing data and software resources is provided that can support investigations in drug combination therapies. Finally, the article concludes with a summary of future directions for investigation by the research community.
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Affiliation(s)
- Vahideh Vakil
- WINLAB, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
| | - Wade Trappe
- WINLAB, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
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15
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Muliadi YK, Huang S, Zhang D, Shi T, Chen L, Mei H. Accurate Prediction of the Adsorption Capabilities of Synthetic Organic Contaminants by Single-Walled Carbon Nanotubes. ChemistrySelect 2019. [DOI: 10.1002/slct.201803406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Yeremia K. Muliadi
- Key Laboratory of Biorheological Science and Technology (Ministry of Education); Chongqing University; Chongqing 400044 China
- College of Bioengineering; Chongqing University; Chongqing 400044 China
| | - Shuheng Huang
- College of Bioengineering; Chongqing University; Chongqing 400044 China
| | - Duo Zhang
- College of Bioengineering; Chongqing University; Chongqing 400044 China
| | - Tingting Shi
- College of Bioengineering; Chongqing University; Chongqing 400044 China
| | - Linxin Chen
- College of Bioengineering; Chongqing University; Chongqing 400044 China
| | - Hu Mei
- Key Laboratory of Biorheological Science and Technology (Ministry of Education); Chongqing University; Chongqing 400044 China
- College of Bioengineering; Chongqing University; Chongqing 400044 China
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16
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Wang T, Chen L, Zhao X. Prediction of Drug Combinations with a Network Embedding Method. Comb Chem High Throughput Screen 2019; 21:789-797. [DOI: 10.2174/1386207322666181226170140] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 11/02/2018] [Accepted: 11/28/2018] [Indexed: 01/10/2023]
Abstract
Aim and Objective:
There are several diseases having a complicated mechanism. For such
complicated diseases, a single drug cannot treat them very well because these diseases always
involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug
combination is an effective way to treat such diseases. However, determination of effective drug
combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and
cheap methods in this regard. Designing effective computational methods incorporating advanced
computational techniques to predict drug combinations is an alternative and feasible way.
Method:
In this study, we proposed a novel network embedding method, which can extract
topological features of each drug combination from a drug network that was constructed using
chemical-chemical interaction information retrieved from STITCH. These topological features were
combined with individual features of drug combination reported in one previous study. Several
advanced computational methods were employed to construct an effective prediction model, such as
synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset,
minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS)
methods that were adopted to analyze features and extract optimal features for building an optimal
support machine vector (SVM) classifier.
Results and Conclusion:
The constructed optimal SVM classifier yielded an MCC of 0.806, which
is superior to the classifier only using individual features with or without SMOTE. The performance
of the classifier can be improved by combining the topological features and essential features of a
drug combination.
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Affiliation(s)
- Tianyun Wang
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Xian Zhao
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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Zhang C, Yan G. Synergistic drug combinations prediction by integrating pharmacological data. Synth Syst Biotechnol 2019; 4:67-72. [PMID: 30820478 PMCID: PMC6370570 DOI: 10.1016/j.synbio.2018.10.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 09/30/2018] [Accepted: 10/04/2018] [Indexed: 12/12/2022] Open
Abstract
There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.
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Affiliation(s)
- Chengzhi Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Guiying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China
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18
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Shokrpour N, Rezaee R, Akbari R, Nasiri M, Foroughinia F. An evaluation of classification algorithms for prediction of drug interactions: Identification of the best algorithm. Int J Pharm Investig 2018. [DOI: 10.4103/jphi.jphi_19_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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