1
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Wang P, Yan F, Dong J, Wang S, Shi Y, Zhu M, Zuo Y, Ma H, Xue R, Zhai D, Song X. A multiple-step screening protocol to identify norepinephrine and dopamine reuptake inhibitors for depression. Phys Chem Chem Phys 2023; 25:8341-8354. [PMID: 36880666 DOI: 10.1039/d2cp05676c] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Depression severely impairs the health of people all over the world. Cognitive dysfunction due to depression has resulted in a severe economic burden to family and society induced by the reduction of social functioning of patients. Norepinephrine-dopamine reuptake inhibitors (NDRIs) targeted with the human norepinephrine transporter (hNET) and distributed with the human dopamine transporter (hDAT) simultaneously treat depression and improve cognitive function, and they effectively prevent sexual dysfunction and other side effects. Because many patients continue to poorly respond to NDRIs, it is urgent to discover novel NDRI antidepressants that do not interfere with cognitive function. The aim of this work was to selectively identify novel NDRI candidates acting against hNET and hDAT from large compound libraries by a comprehensive strategy integrating support vector machine (SVM) models, ADMET, molecular docking, in vitro binding assays, molecular dynamics simulation, and binding energy calculation. First, 6522 compounds that do not inhibit the human serotonin transporter (hSERT) were obtained by SVM models of hNET, hDAT, and non-target hSERT with similarity analyses from compound libraries. ADMET and molecular docking were then used to identify compounds that could potently bind to the hNET and hDAT with satisfactory ADMET, and 4 compounds were successfully identified. According to their docking scores and ADMET information, 3719810 was advanced for profiling by in vitro assays as a novel NDRI lead compound due to its strongest druggability and balancing activities. Encouragingly, 3719810 performed comparative activities on two targets, with Ki values of 7.32 μM for hNET and 5.23 μM for hDAT. To obtain candidates with additional activities and balance the activities of 2 targets, 5 analogs were optimized, and 2 novel scaffold compounds were successively designed. By assessment of molecular docking, molecular dynamics simulations, and binding energy calculations, 5 compounds were validated as NDRI candidates with high activities, and 4 of them performed acceptable balancing activities acting on hNET and hDAT. This work supplied promising novel NDRIs for treatment of depression with cognitive dysfunction or other related neurodegenerative disorders, and also provided a strategy for highly efficient and cost-effective identification of inhibitors for dual targets with homologous non-targets.
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Affiliation(s)
- Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Fengmei Yan
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Jianghong Dong
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Shengqiang Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Yu Shi
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Mengdan Zhu
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Yuting Zuo
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Hui Ma
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Ruirui Xue
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Dingjie Zhai
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Xiaoyu Song
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
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2
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Luo Y, Wang P, Mou M, Zheng H, Hong J, Tao L, Zhu F. A novel strategy for designing the magic shotguns for distantly related target pairs. Brief Bioinform 2023; 24:6984790. [PMID: 36631399 DOI: 10.1093/bib/bbac621] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/09/2022] [Accepted: 12/17/2022] [Indexed: 01/13/2023] Open
Abstract
Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.
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Affiliation(s)
- Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hanqi Zheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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3
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Target-Based Small Molecule Drug Discovery for Colorectal Cancer: A Review of Molecular Pathways and In Silico Studies. Biomolecules 2022; 12:biom12070878. [PMID: 35883434 PMCID: PMC9312989 DOI: 10.3390/biom12070878] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/05/2022] [Accepted: 06/17/2022] [Indexed: 01/27/2023] Open
Abstract
Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.
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Sowrirajan S, Elangovan N, Ajithkumar G, Manoj KP. (E)-4-((4-Bromobenzylidene) Amino)-N-(Pyrimidin-2-yl) Benzenesulfonamide from 4-Bromobenzaldehyde and Sulfadiazine, Synthesis, Spectral (FTIR, UV–Vis), Computational (DFT, HOMO–LUMO, MEP, NBO, NPA, ELF, LOL, RDG) and Molecular Docking Studies. Polycycl Aromat Compd 2022. [DOI: 10.1080/10406638.2021.2006245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- S. Sowrirajan
- Department of Chemistry, King Fahd University of Petroleum and Minerals, Kingdom of Saudi Arabia
| | - N. Elangovan
- Department of Chemistry, Arignar Anna Government Arts College (Affiliated to Bharathidasan University), Musiri, Tiruchirappalli, Tamil Nadu, India
| | - G. Ajithkumar
- Department of Chemistry, Arignar Anna Government Arts College (Affiliated to Bharathidasan University), Musiri, Tiruchirappalli, Tamil Nadu, India
| | - K. P. Manoj
- Department of Chemistry, Arignar Anna Government Arts College (Affiliated to Bharathidasan University), Musiri, Tiruchirappalli, Tamil Nadu, India
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5
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Elangovan N, Sowrirajan S. Synthesis, single crystal (XRD), Hirshfeld surface analysis, computational study (DFT) and molecular docking studies of (E)-4-((2-hydroxy-3,5-diiodobenzylidene)amino)-N-(pyrimidine)-2-yl) benzenesulfonamide. Heliyon 2021; 7:e07724. [PMID: 34458601 PMCID: PMC8379672 DOI: 10.1016/j.heliyon.2021.e07724] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/14/2021] [Accepted: 08/03/2021] [Indexed: 12/15/2022] Open
Abstract
The Schiff base (E)-4-((2-hydroxy-3,5-diiodobenzylidene)amino)-N-(pyrimidine)-2-yl) benzene sulfonamide (DIDA) compound was synthesis with condensation of 3,5-diiodosalicylaldehyde and sulfadiazine. The compound characterized with FTIR, X-ray crystallography and electronic spectra. The titled compound associated with experimental and theoretical method, DFT used for the theoretical method. The IR was calculated from DFT mode with B3LYP/GENSEP basic set. The electronic spectra computed from TD-DFT method with CAM-B3LYP functional, with IEFPCM solvation model and DMSO used as the solvent. Wave function based properties like localized orbital locator, electron localization function and non-covalent interactions have been studied extensively. The ADMET properties of the compound DIDA indicated that the compound has excellent drug likeness properties and PASS studies showed that it has anti-infective properties, which is confirmed by a docking score of -7.4 kcal/mol.
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Affiliation(s)
- N Elangovan
- Department of Chemistry, Arignar Anna Government Arts College, Musiri 621211, Bharathidasan University, Tiruchirappalli, Tamilnadu, India
| | - S Sowrirajan
- Department of Chemistry, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
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Krishna S, Lakra AD, Shukla N, Khan S, Mishra DP, Ahmed S, Siddiqi MI. Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation. J Biomol Struct Dyn 2019; 38:3280-3295. [DOI: 10.1080/07391102.2019.1654925] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Shagun Krishna
- Molecular & Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Amar Deep Lakra
- Endocrinology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Nidhi Shukla
- Endocrinology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Saman Khan
- Molecular & Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Durga Prasad Mishra
- Endocrinology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Shakil Ahmed
- Molecular & Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Mohammad Imran Siddiqi
- Molecular & Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
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7
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Liu XG, Lv MC, Huang MY, Sun YQ, Gao PY, Li DQ. A network pharmacology study on the triterpene saponins from Medicago sativa L. for the treatment of Neurodegenerative diseases. J Food Biochem 2019; 43:e12955. [PMID: 31368545 DOI: 10.1111/jfbc.12955] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 05/29/2019] [Accepted: 05/31/2019] [Indexed: 12/26/2022]
Abstract
Neurodegenerative diseases (NDDs) are characterized by progressive and irreversible, is a kind of complex illnesses, and the long-term therapy which is frequently associated with adverse side effects. Medicago sativa L., widely consumed as a vegetable, has the effects of improving memory and relieving central nervous system diseases. However, there are less studies on its specific mechanism for NDDs. In this investigation, we applied a method of network pharmacology, which combined molecular docking and network analysis to decipher the mechanisms of M. sativa in NDDs. The pharmacological system generated 55 triterpene saponins from M. sativa, and predicted 27 potential targets with 100 pathways in the treatment of NDDs. As a result, 13 compounds, 10 target proteins, and 6 signaling pathways were found to play important roles in the treatment of NDDs. In addition, in vitro experiments of isolates confirmed activities for NDDs, which were consistent with the results of network pharmacology prediction. PRACTICAL APPLICATIONS: Medicago sativa L. has been widely consumed as a vegetable, which possesses many nutritional components. As a functional food stuff, M. sativa can improve human health, such as memory improving activities, relieving central nervous system diseases, immunomodulatory, antioxidant, anticancer, and anti-inflammatory. In this article, the mechanism of triterpene saponins from M. sativa against NDDs was successfully predicted by network pharmacology method. The results will serve as a reference of M. sativa against NDDs.
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Affiliation(s)
- Xue-Gui Liu
- College of Pharmaceutical and Biological Engineering, Shenyang University of Chemical Technology, Shenyang, P.R. China.,Institute of Functional Molecules, Shenyang University of Chemical Technology, Shenyang, P.R. China
| | - Meng-Chao Lv
- College of Pharmaceutical and Biological Engineering, Shenyang University of Chemical Technology, Shenyang, P.R. China
| | - Ming-Yuan Huang
- Shenyang Institute of Science and Technology, Shenyang, P.R. China
| | - Yu-Qiu Sun
- College of Pharmaceutical and Biological Engineering, Shenyang University of Chemical Technology, Shenyang, P.R. China
| | - Pin-Yi Gao
- College of Pharmaceutical and Biological Engineering, Shenyang University of Chemical Technology, Shenyang, P.R. China.,Institute of Functional Molecules, Shenyang University of Chemical Technology, Shenyang, P.R. China
| | - Dan-Qi Li
- Institute of Functional Molecules, Shenyang University of Chemical Technology, Shenyang, P.R. China
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8
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Batool M, Ahmad B, Choi S. A Structure-Based Drug Discovery Paradigm. Int J Mol Sci 2019; 20:ijms20112783. [PMID: 31174387 PMCID: PMC6601033 DOI: 10.3390/ijms20112783] [Citation(s) in RCA: 264] [Impact Index Per Article: 52.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 05/31/2019] [Accepted: 06/04/2019] [Indexed: 12/14/2022] Open
Abstract
Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.
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Affiliation(s)
- Maria Batool
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, Korea.
| | - Bilal Ahmad
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, Korea.
| | - Sangdun Choi
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, Korea.
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9
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Krishna S, Kumar S, Singh DK, Lakra AD, Banerjee D, Siddiqi MI. Multiple Machine Learning Based-Chemoinformatics Models for Identification of Histone Acetyl Transferase Inhibitors. Mol Inform 2018; 37:e1700150. [DOI: 10.1002/minf.201700150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 04/06/2018] [Indexed: 01/25/2023]
Affiliation(s)
- Shagun Krishna
- Molecular & Structural Biology Division; CSIR-Central Drug Research Institute; Lucknow India 260031
| | - Sushil Kumar
- Molecular & Structural Biology Division; CSIR-Central Drug Research Institute; Lucknow India 260031
| | - Deependra Kumar Singh
- Molecular & Structural Biology Division; CSIR-Central Drug Research Institute; Lucknow India 260031
| | - Amar Deep Lakra
- Endocrinology Division; CSIR-Central Drug Research Institute; Lucknow India 260031
| | - Dibyendu Banerjee
- Molecular & Structural Biology Division; CSIR-Central Drug Research Institute; Lucknow India 260031
| | - Mohammad Imran Siddiqi
- Molecular & Structural Biology Division; CSIR-Central Drug Research Institute; Lucknow India 260031
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10
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Chandra S, Pandey J, Tamrakar AK, Siddiqi MI. SVMDLF: A novel R-based Web application for prediction of dipeptidyl peptidase 4 inhibitors. Chem Biol Drug Des 2017; 90:1173-1183. [PMID: 28585374 DOI: 10.1111/cbdd.13037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 04/07/2017] [Accepted: 04/08/2017] [Indexed: 12/15/2022]
Abstract
Dipeptidyl peptidase 4 (DPP4) is a well-known target for the antidiabetic drugs. However, currently available DPP4 inhibitor screening assays are costly and labor-intensive. It is important to create a robust in silico method to predict the activity of DPP4 inhibitor for the new lead finding. Here, we introduce an R-based Web application SVMDLF (SVM-based DPP4 Lead Finder) to predict the inhibitor of DPP4, based on support vector machine (SVM) model, predictions of which are confirmed by in vitro biological evaluation. The best model generated by MACCS structure fingerprint gave the Matthews correlation coefficient of 0.87 for the test set and 0.883 for the external test set. We screened Maybridge database consisting approximately 53,000 compounds. For further bioactivity assay, six compounds were shortlisted, and of six hits, three compounds showed significant DPP4 inhibitory activities with IC50 values ranging from 8.01 to 10.73 μm. This application is an OpenCPU server app which is a novel single-page R-based Web application for the DPP4 inhibitor prediction. The SVMDLF is freely available and open to all users at http://svmdlf.net/ocpu/library/dlfsvm/www/ and http://www.cdri.res.in/svmdlf/.
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Affiliation(s)
- Sharat Chandra
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Jyotsana Pandey
- Biochemistry Division, CSIR-Central Drug Research Institute, Lucknow, India
| | | | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
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11
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Shen W, Xiao T, Chen S, Liu F, Chen YZ, Jiang Y. Predicting the Enzymatic Hydrolysis Half‐lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination. Mol Inform 2017. [DOI: 10.1002/minf.201600153] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Wanxiang Shen
- Department of ChemistryTsinghua University Beijing 100084 P. R. China
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
| | - Tao Xiao
- Department of ChemistryTsinghua University Beijing 100084 P. R. China
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
| | - Shangying Chen
- Bioinformatics and Drug Design Group, Department of PharmacyNational University of Singapore Singapore 117543 Singapore
| | - Feng Liu
- Department of ChemistryTsinghua University Beijing 100084 P. R. China
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
| | - Yu Zong Chen
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
- Bioinformatics and Drug Design Group, Department of PharmacyNational University of Singapore Singapore 117543 Singapore
- Shenzhen Kivita Innovative Drug Discovery Institute Shenzhen 518055 P. R. China
| | - Yuyang Jiang
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
- School of Pharmaceutical SciencesTsinghua University Beijing 100084 P. R. China
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12
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Chandra S, Pandey J, Tamrakar AK, Siddiqi MI. Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B inhibitors. J Mol Graph Model 2016; 71:242-256. [PMID: 28006676 DOI: 10.1016/j.jmgm.2016.10.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 09/27/2016] [Accepted: 10/25/2016] [Indexed: 12/21/2022]
Abstract
In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries.
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Affiliation(s)
- Sharat Chandra
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Resaerch Institute, Campus, Lucknow 226031, India; Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow 226031, India
| | - Jyotsana Pandey
- Biochemistry Division, CSIR-Central Drug Research Institute, Lucknow 226031, India
| | | | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Resaerch Institute, Campus, Lucknow 226031, India; Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow 226031, India.
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13
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Singh VK, Chang HH, Kuo CC, Shiao HY, Hsieh HP, Coumar MS. Drug repurposing for chronic myeloid leukemia: in silico and in vitro investigation of DrugBank database for allosteric Bcr-Abl inhibitors. J Biomol Struct Dyn 2016; 35:1833-1848. [DOI: 10.1080/07391102.2016.1196462] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Vivek Kumar Singh
- School of Life Sciences, Centre for Bioinformatics, Pondicherry University, Kalapet, Puducherry 605014, India
| | - Hsin-Huei Chang
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Ching-Chuan Kuo
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, National Cheng Kung University Medical College, Tainan, Taiwan
- Graduate Program for Aging, China Medical University, Taichung, Taiwan, ROC
| | - Hui-Yi Shiao
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Hsing-Pang Hsieh
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
- Department of Chemistry, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Mohane Selvaraj Coumar
- School of Life Sciences, Centre for Bioinformatics, Pondicherry University, Kalapet, Puducherry 605014, India
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14
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Chen S, Zhang P, Liu X, Qin C, Tao L, Zhang C, Yang SY, Chen YZ, Chui WK. Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach. J Mol Graph Model 2016; 67:102-10. [PMID: 27262528 DOI: 10.1016/j.jmgm.2016.05.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 05/17/2016] [Accepted: 05/18/2016] [Indexed: 02/05/2023]
Abstract
The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates.
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Affiliation(s)
- Shangying Chen
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore
| | - Peng Zhang
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore
| | - Xin Liu
- Shanghai Applied Protein Technology Co. Ltd, Research Center for Proteome Analysis, Institute of Biochemistry and cell Biology, Shanghai Institutes for Biological Sciences, Shanghai, 200233, China
| | - Chu Qin
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore
| | - Lin Tao
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore
| | - Cheng Zhang
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore
| | - Sheng Yong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan, China
| | - Yu Zong Chen
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore.
| | - Wai Keung Chui
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore.
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Wei Y, Li J, Chen Z, Wang F, Huang W, Hong Z, Lin J. Multistage virtual screening and identification of novel HIV-1 protease inhibitors by integrating SVM, shape, pharmacophore and docking methods. Eur J Med Chem 2015; 101:409-18. [PMID: 26185005 DOI: 10.1016/j.ejmech.2015.06.054] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2015] [Revised: 06/28/2015] [Accepted: 06/29/2015] [Indexed: 11/30/2022]
Abstract
The HIV-1 protease has proven to be a crucial component of the HIV replication machinery and a reliable target for anti-HIV drug discovery. In this study, we applied an optimized hierarchical multistage virtual screening method targeting HIV-1 protease. The method sequentially applied SVM (Support Vector Machine), shape similarity, pharmacophore modeling and molecular docking. Using a validation set (270 positives, 155,996 negatives), the multistage virtual screening method showed a high hit rate and high enrichment factor of 80.47% and 465.75, respectively. Furthermore, this approach was applied to screen the National Cancer Institute database (NCI), which contains 260,000 molecules. From the final hit list, 6 molecules were selected for further testing in an in vitro HIV-1 protease inhibitory assay, and 2 molecules (NSC111887 and NSC121217) showed inhibitory potency against HIV-1 protease, with IC50 values of 62 μM and 162 μM, respectively. With further chemical development, these 2 molecules could potentially serve as HIV-1 protease inhibitors.
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Affiliation(s)
- Yu Wei
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, PR China; College of Pharmacy, Nankai University, Tianjin 300071, PR China
| | - Jinlong Li
- College of Pharmacy, Nankai University, Tianjin 300071, PR China
| | - Zeming Chen
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, PR China; College of Life Sciences, Nankai University, Tianjin 300071, PR China
| | - Fengwei Wang
- Department of Oncology, Tianjin Union Medical Center, Tianjin 300180, PR China
| | | | - Zhangyong Hong
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, PR China; College of Life Sciences, Nankai University, Tianjin 300071, PR China.
| | - Jianping Lin
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, PR China; College of Pharmacy, Nankai University, Tianjin 300071, PR China.
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16
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Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools. Adv Drug Deliv Rev 2015; 86:83-100. [PMID: 26037068 DOI: 10.1016/j.addr.2015.03.014] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 03/18/2015] [Accepted: 03/22/2015] [Indexed: 02/05/2023]
Abstract
In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties.
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Xu HL, Wang ZJ, Liang XM, Li X, Shi Z, Zhou N, Bao JK. In silico identification of novel kinase inhibitors targeting wild-type and T315I mutant ABL1 from FDA-approved drugs. MOLECULAR BIOSYSTEMS 2014; 10:1524-37. [PMID: 24691568 DOI: 10.1039/c3mb70577c] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The constitutively active fusion protein BCR-ABL1 is the major cause of chronic myeloid leukemia (CML), and selective inhibition of ABL1 is a promising approach for the treatment of CML. Reported drugs worked well in clinical practice, such as imatinib, dasatinib, nilotinib and bosutinib. However, resistance arises due to ABL1 mutation in patients, especially the T315I gate-keeper mutation. Thus, wide spectrum drugs targeting ABL1 are urgently needed. In order to screen potential drugs targeting wild-type ABL1 and T315I mutant ABL1, 1408 FDA approved small molecule drugs were subjected to molecular docking. With subsequent molecular dynamic (MD) simulation and MM/GBSA binding free energy calculation and energy decomposition, we identified chlorhexidine and sorafenib as potential "new use" drugs targeting wild-type ABL1, while nicergoline and plerixafor targeted T315I ABL1. Meanwhile, we also found that residues located in the ATP-binding site and A-loop motif played key roles in drug discovery towards ABL1. These findings may not only serve as a paradigm for the repositioning of existing approved drugs, but also instill new vitality to ABL1-targeted anti-CML therapeutics.
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Affiliation(s)
- Huai-long Xu
- School of Life Sciences and Key laboratory of Bio-resources, Ministry of Education, Sichuan University, Chengdu 610064, China.
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18
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Fang J, Yang R, Gao L, Zhou D, Yang S, Liu AL, Du GH. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. J Chem Inf Model 2013; 53:3009-20. [PMID: 24144102 DOI: 10.1021/ci400331p] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.
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Affiliation(s)
- Jiansong Fang
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050, People's Republic of China
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Chen J, Liu Y, Cheng T, Lao X, Gao X, Zheng H, Yao W. A common binding mode that may facilitate the design of novel broad-spectrum inhibitors against metallo-β-lactamases. Med Chem Res 2013. [DOI: 10.1007/s00044-013-0646-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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20
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Smusz S, Kurczab R, Bojarski AJ. The influence of the inactives subset generation on the performance of machine learning methods. J Cheminform 2013; 5:17. [PMID: 23561266 PMCID: PMC3626618 DOI: 10.1186/1758-2946-5-17] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2013] [Accepted: 03/25/2013] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND A growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years. However, their effectiveness is strongly dependent on many different factors. RESULTS In this study, the influence of the way of forming the set of inactives on the classification process was examined: random and diverse selection from the ZINC database, MDDR database and libraries generated according to the DUD methodology. All learning methods were tested in two modes: using one test set, the same for each method of inactive molecules generation and using test sets with inactives prepared in an analogous way as for training. The experiments were carried out for 5 different protein targets, 3 fingerprints for molecules representation and 7 classification algorithms with varying parameters. It appeared that the process of inactive set formation had a substantial impact on the machine learning methods performance. CONCLUSIONS The level of chemical space limitation determined the ability of tested classifiers to select potentially active molecules in virtual screening tasks, as for example DUDs (widely applied in docking experiments) did not provide proper selection of active molecules from databases with diverse structures. The study clearly showed that inactive compounds forming training set should be representative to the highest possible extent for libraries that undergo screening.
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Affiliation(s)
- Sabina Smusz
- Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, Kraków, 31-343, Poland
- Faculty of Chemistry, Jagiellonian University, R. Ingardena 3, Kraków, 30-060, Poland
| | - Rafał Kurczab
- Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, Kraków, 31-343, Poland
| | - Andrzej J Bojarski
- Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, Kraków, 31-343, Poland
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21
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Qing XY, Zhang CH, Li LL, Ji P, Ma S, Wan HL, Wang ZR, Zou J, Yang SY. Retrieving novel C5aR antagonists using a hybrid ligand-based virtual screening protocol based on SVM classification and pharmacophore models. J Biomol Struct Dyn 2013; 31:215-23. [DOI: 10.1080/07391102.2012.698245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Zhang J, Han B, Wei X, Tan C, Chen Y, Jiang Y. A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands. PLoS One 2012; 7:e39076. [PMID: 22720033 PMCID: PMC3376116 DOI: 10.1371/journal.pone.0039076] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2011] [Accepted: 05/15/2012] [Indexed: 01/13/2023] Open
Abstract
Target selective drugs, such as dopamine receptor (DR) subtype selective ligands, are developed for enhanced therapeutics and reduced side effects. In silico methods have been explored for searching DR selective ligands, but encountered difficulties associated with high subtype similarity and ligand structural diversity. Machine learning methods have shown promising potential in searching target selective compounds. Their target selective capability can be further enhanced. In this work, we introduced a new two-step support vector machines target-binding and selectivity screening method for searching DR subtype-selective ligands, which was tested together with three previously-used machine learning methods for searching D1, D2, D3 and D4 selective ligands. It correctly identified 50.6%–88.0% of the 21–408 subtype selective and 71.7%–81.0% of the 39–147 multi-subtype ligands. Its subtype selective ligand identification rates are significantly better than, and its multi-subtype ligand identification rates are comparable to the best rates of the previously used methods. Our method produced low false-hit rates in screening 13.56 M PubChem, 168,016 MDDR and 657,736 ChEMBLdb compounds. Molecular features important for subtype selectivity were extracted by using the recursive feature elimination feature selection method. These features are consistent with literature-reported features. Our method showed similar performance in searching estrogen receptor subtype selective ligands. Our study demonstrated the usefulness of the two-step target binding and selectivity screening method in searching subtype selective ligands from large compound libraries.
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Affiliation(s)
- Jingxian Zhang
- The Key Laboratory of Chemical Biology, Guangdong Province, Graduate School at Shenzhen, Tsinghua University, Shenzhen, People's Republic of China
- Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Singapore, Singapore
| | - Bucong Han
- Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Singapore, Singapore
- Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore, Singapore, Singapore
| | - Xiaona Wei
- Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Singapore, Singapore
- Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore, Singapore, Singapore
| | - Chunyan Tan
- The Key Laboratory of Chemical Biology, Guangdong Province, Graduate School at Shenzhen, Tsinghua University, Shenzhen, People's Republic of China
| | - Yuzong Chen
- The Key Laboratory of Chemical Biology, Guangdong Province, Graduate School at Shenzhen, Tsinghua University, Shenzhen, People's Republic of China
- Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Singapore, Singapore
- * E-mail: (YZC); (YYJ)
| | - Yuyang Jiang
- The Key Laboratory of Chemical Biology, Guangdong Province, Graduate School at Shenzhen, Tsinghua University, Shenzhen, People's Republic of China
- * E-mail: (YZC); (YYJ)
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Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries. J Mol Graph Model 2012; 32:49-66. [DOI: 10.1016/j.jmgm.2011.09.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2011] [Revised: 08/30/2011] [Accepted: 09/01/2011] [Indexed: 12/13/2022]
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24
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Ren JX, Li LL, Zheng RL, Xie HZ, Cao ZX, Feng S, Pan YL, Chen X, Wei YQ, Yang SY. Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking. J Chem Inf Model 2011; 51:1364-75. [PMID: 21618971 DOI: 10.1021/ci100464b] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based VS (PB-VS), and docking-based VS (DB-VS) methods. In this approach, the three VS methods are applied in an increasing order of complexity so that the first filter (SB-VS) is fast and simple, while successive ones (PB-VS and DB-VS) are more time-consuming but are applied only to a small subset of the entire database. Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor. This approach was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database. From the final hits, 47 compounds were selected for further in vitro Pim-1 inhibitory assay, and 15 compounds show nanomolar level or low micromolar inhibition potency against Pim-1. In particular, four of them were found to have new scaffolds which have potential for the chemical development of Pim-1 inhibitors.
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Affiliation(s)
- Ji-Xia Ren
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China
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25
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Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries. J Comput Aided Mol Des 2011; 25:455-67. [DOI: 10.1007/s10822-011-9431-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2010] [Accepted: 04/17/2011] [Indexed: 10/18/2022]
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26
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Zhang C, Tan C, Zu X, Zhai X, Liu F, Chu B, Ma X, Chen Y, Gong P, Jiang Y. Exploration of (S)-3-aminopyrrolidine as a potentially interesting scaffold for discovery of novel Abl and PI3K dual inhibitors. Eur J Med Chem 2011; 46:1404-14. [DOI: 10.1016/j.ejmech.2011.01.020] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2010] [Revised: 12/29/2010] [Accepted: 01/12/2011] [Indexed: 01/04/2023]
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27
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Li GB, Yang LL, Feng S, Zhou JP, Huang Q, Xie HZ, Li LL, Yang SY. Discovery of novel mGluR1 antagonists: a multistep virtual screening approach based on an SVM model and a pharmacophore hypothesis significantly increases the hit rate and enrichment factor. Bioorg Med Chem Lett 2011; 21:1736-40. [PMID: 21316965 DOI: 10.1016/j.bmcl.2011.01.087] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2010] [Revised: 01/17/2011] [Accepted: 01/19/2011] [Indexed: 02/05/2023]
Abstract
Development of glutamate non-competitive antagonists of mGluR1 (Metabotropic glutamate receptor subtype 1) has increasingly attracted much attention in recent years due to their potential therapeutic application for various nervous disorders. Since there is no crystal structure reported for mGluR1, ligand-based virtual screening (VS) methods, typically pharmacophore-based VS (PB-VS), are often used for the discovery of mGluR1 antagonists. Nevertheless, PB-VS usually suffers a lower hit rate and enrichment factor. In this investigation, we established a multistep ligand-based VS approach that is based on a support vector machine (SVM) classification model and a pharmacophore model. Performance evaluation of these methods in virtual screening against a large independent test set, M-MDDR, show that the multistep VS approach significantly increases the hit rate and enrichment factor compared with the individual SB-VS and PB-VS methods. The multistep VS approach was then used to screen several large chemical libraries including PubChem, Specs, and Enamine. Finally a total of 20 compounds were selected from the top ranking compounds, and shifted to the subsequent in vitro and in vivo studies, which results will be reported in the near future.
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Affiliation(s)
- Guo-Bo Li
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan, China
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28
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Abstract
IMPORTANCE OF THE FIELD: PubChem is a public molecular information repository, a scientific showcase of the NIH Roadmap Initiative. The PubChem database holds over 27 million records of unique chemical structures of compounds (CID) derived from nearly 70 million substance depositions (SID), and contains more than 449,000 bioassay records with over thousands of in vitro biochemical and cell-based screening bioassays established, with targeting more than 7000 proteins and genes linking to over 1.8 million of substances. AREAS COVERED IN THIS REVIEW: This review builds on recent PubChem-related computational chemistry research reported by other authors while providing readers with an overview of the PubChem database, focusing on its increasing role in cheminformatics, virtual screening and toxicity prediction modeling. WHAT THE READER WILL GAIN: These publicly available datasets in PubChem provide great opportunities for scientists to perform cheminformatics and virtual screening research for computer-aided drug design. However, the high volume and complexity of the datasets, in particular the bioassay-associated false positives/negatives and highly imbalanced datasets in PubChem, also creates major challenges. Several approaches regarding the modeling of PubChem datasets and development of virtual screening models for bioactivity and toxicity predictions are also reviewed. TAKE HOME MESSAGE: Novel data-mining cheminformatics tools and virtual screening algorithms are being developed and used to retrieve, annotate and analyze the large-scale and highly complex PubChem biological screening data for drug design.
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Affiliation(s)
- Xiang-Qun Xie
- Department of Pharmaceutical Sciences, School of Pharmacy; Drug Discovery Institute/Pittsburgh Molecular Library Screening Center (PMLSC); Pittsburgh Chemical Methodologies & Library Development (PCMLD) Center; Departments of Computational Biology and Structural Biology; University of Pittsburgh, Pittsburgh, PA 15260, USA
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29
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Michielan L, Moro S. Pharmaceutical Perspectives of Nonlinear QSAR Strategies. J Chem Inf Model 2010; 50:961-78. [DOI: 10.1021/ci100072z] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Lisa Michielan
- Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche, Università di Padova, via Marzolo 5, I-35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche, Università di Padova, via Marzolo 5, I-35131 Padova, Italy
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30
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Geppert H, Vogt M, Bajorath J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 2010; 50:205-16. [PMID: 20088575 DOI: 10.1021/ci900419k] [Citation(s) in RCA: 231] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Hanna Geppert
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universitat, Dahlmannstrasse 2, D-53113 Bonn, Germany
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31
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Ma XH, Shi Z, Tan C, Jiang Y, Go ML, Low BC, Chen YZ. In-silico approaches to multi-target drug discovery : computer aided multi-target drug design, multi-target virtual screening. Pharm Res 2010; 27:739-49. [PMID: 20221898 DOI: 10.1007/s11095-010-0065-2] [Citation(s) in RCA: 96] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2009] [Accepted: 01/08/2010] [Indexed: 01/25/2023]
Abstract
Multi-target drugs against selective multiple targets improve therapeutic efficacy, safety and resistance profiles by collective regulations of a primary therapeutic target together with compensatory elements and resistance activities. Efforts have been made to employ in-silico methods for facilitating the search and design of selective multi-target agents. These methods have shown promising potential in facilitating drug discovery directed at selective multiple targets.
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Affiliation(s)
- Xiao Hua Ma
- Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore, 117543, Singapore
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