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Shi Y, Bao XY. QSPR Modeling for the Prediction of the Triplet Yield of Singlet Fission Materials. JOURNAL OF SAUDI CHEMICAL SOCIETY 2023. [DOI: 10.1016/j.jscs.2023.101614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Hermansyah O, Bustamam A, Yanuar A. Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds. Comput Biol Chem 2021; 95:107597. [PMID: 34800858 DOI: 10.1016/j.compbiolchem.2021.107597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 12/31/2022]
Abstract
Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure-activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R2pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC50 value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.
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Affiliation(s)
- Oky Hermansyah
- Laboratory of Biomedical Computation and Drug Design, Faculty of Pharmacy, Universitas Indonesia, Depok 16424, Indonesia
| | - Alhadi Bustamam
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia
| | - Arry Yanuar
- Laboratory of Biomedical Computation and Drug Design, Faculty of Pharmacy, Universitas Indonesia, Depok 16424, Indonesia.
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Zhao J, Xu P, Liu X, Ji X, Li M, Dev S, Qu X, Lu W, Niu B. Application of machine learning methods for the development of antidiabetic drugs. Curr Pharm Des 2021; 28:260-271. [PMID: 34161205 DOI: 10.2174/1381612827666210622104428] [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: 11/26/2020] [Accepted: 05/10/2021] [Indexed: 11/22/2022]
Abstract
Diabetes is a chronic non-communicable disease caused by several different routes, which has attracted increasing attention. In order to speed up the development of new selective drugs, machine learning (ML) technology has been applied in the process of diabetes drug development, which opens up a new blueprint for drug design. This review provides a comprehensive portrayal of the application of ML in antidiabetic drug use.
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Affiliation(s)
- Juanjuan Zhao
- Department of Chemistry, College of Sciences, Shanghai University, 200444, China
| | - Pengcheng Xu
- Materials Genome Institute, Shanghai University, Shanghai 200444, China
| | - Xiujuan Liu
- Department of Chemistry, College of Sciences, Shanghai University, 200444, China
| | - Xiaobo Ji
- Department of Chemistry, College of Sciences, Shanghai University, 200444, China
| | - Minjie Li
- Department of Chemistry, College of Sciences, Shanghai University, 200444, China
| | - Sooranna Dev
- Department of Obstetrics and Gynaecology, Imperial College London, Fulham Road, London SW10 9 NH, United Kingdom
| | - Xiaosheng Qu
- National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, No. 189, Changgang Road, 530023, Nanning, China
| | - Wencong Lu
- Department of Chemistry, College of Sciences, Shanghai University, 200444, China
| | - Bing Niu
- School of Life Sciences, Shanghai University, 200444, China
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Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds. Sci Rep 2021; 11:8806. [PMID: 33888843 PMCID: PMC8062522 DOI: 10.1038/s41598-021-88341-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/12/2021] [Indexed: 12/15/2022] Open
Abstract
The Support vector regression (SVR) was used to investigate quantitative structure-activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.
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Shi L, Chang D, Ji X, Lu W. Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area. J Chem Inf Model 2018; 58:2420-2427. [DOI: 10.1021/acs.jcim.8b00436] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Li Shi
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
| | - Dongping Chang
- Materials Genome Institute, Shanghai University, Shanghai 200444, China
| | - Xiaobo Ji
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
| | - Wencong Lu
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
- Materials Genome Institute, Shanghai University, Shanghai 200444, China
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Patel BD, Ghate MD. 3D-QSAR studies of dipeptidyl peptidase-4 inhibitors using various alignment methods. Med Chem Res 2014. [DOI: 10.1007/s00044-014-1178-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Gu T, Yang X, Li M, Wu M, Su Q, Lu W, Zhang Y. Predicting the DPP-IV inhibitory activity pIC₅₀ based on their physicochemical properties. BIOMED RESEARCH INTERNATIONAL 2013; 2013:798743. [PMID: 23865065 PMCID: PMC3705804 DOI: 10.1155/2013/798743] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2013] [Revised: 05/10/2013] [Accepted: 05/28/2013] [Indexed: 11/17/2022]
Abstract
The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50 of the DPP-IV inhibitors as described in this paper has been given in the introduction.
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Affiliation(s)
- Tianhong Gu
- School of Materials Science and Engineering, Shanghai University, 149 Yan-Chang Road, Shanghai 200072, China
| | - Xiaoyan Yang
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
| | - Minjie Li
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
| | - Milin Wu
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
| | - Qiang Su
- School of Materials Science and Engineering, Shanghai University, 149 Yan-Chang Road, Shanghai 200072, China
| | - Wencong Lu
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
| | - Yuhui Zhang
- Department of Neurosurgery, Changhai Hospital, Second Military Medical University, 168 Chang-Hai Road, Shanghai 200433, China
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