1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Rodríguez-Pérez R, Bajorath J. Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery. J Comput Aided Mol Des 2022; 36:355-362. [PMID: 35304657 PMCID: PMC9325859 DOI: 10.1007/s10822-022-00442-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 02/15/2022] [Indexed: 11/05/2022]
Abstract
The support vector machine (SVM) algorithm is one of the most widely used machine learning (ML) methods for predicting active compounds and molecular properties. In chemoinformatics and drug discovery, SVM has been a state-of-the-art ML approach for more than a decade. A unique attribute of SVM is that it operates in feature spaces of increasing dimensionality. Hence, SVM conceptually departs from the paradigm of low dimensionality that applies to many other methods for chemical space navigation. The SVM approach is applicable to compound classification, and ranking, multi-class predictions, and –in algorithmically modified form– regression modeling. In the emerging era of deep learning (DL), SVM retains its relevance as one of the premier ML methods in chemoinformatics, for reasons discussed herein. We describe the SVM methodology including strengths and weaknesses and discuss selected applications that have contributed to the evolution of SVM as a premier approach for compound classification, property predictions, and virtual compound screening.
Collapse
Affiliation(s)
- Raquel Rodríguez-Pérez
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115, Bonn, Germany.,Novartis Institutes for Biomedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115, Bonn, Germany. .,Novartis Institutes for Biomedical Research, Novartis Campus, CH-4002, Basel, Switzerland.
| |
Collapse
|
3
|
Wang P, Gao X, Zhang K, Pei Q, Xu X, Yan F, Dong J, Jing C. Exploring the binding mechanism of positive allosteric modulators in human metabotropic glutamate receptor 2 using molecular dynamics simulations. Phys Chem Chem Phys 2021; 23:24125-24139. [PMID: 34596645 DOI: 10.1039/d1cp02157e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Positive allosteric modulators (PAMs) of human metabotropic glutamate receptor 2 (hmGlu2) are well-known in the treatment of psychiatric disorders for their higher selectivity and lower tolerance risk. A variety of PAMs have been reported over the last decade and two compounds were in Phase II clinical trials for schizophrenia and anxiety. These trials were discontinued on account of the unsatisfactory therapeutic efficacy, but PAMs were explored as novel treatments for addiction and epilepsy. Thus, it is still important to explore novel hmGlu2 PAMs in the near future. Nowadays, the challenges in optimizing drug potency and improving scaffold diversity for PAMs are the noncomprehensive character analyses of multiple scaffolds; the exploration of the binding modes of PAMs in the allosteric binding site have been proposed to reduce this difficulty. However, there has been no comprehensive research about the binding profiles of PAMs in the hmGlu2 receptor. To address this issue, this work explores the binding characters of eight PAMs representing five chemical series by multiple computational methods. As a result, the shared binding modes of the eight studied PAMs interacting with 15 residues in the allosteric binding site were defined. In addition, the reduced hydrophobicity with low electronegativity of R1, increased hydrophobicity with low negative electron density of R2 and the electronegativity of the linker were identified as indicators that regulate the affinity of PAMs. This finding agrees well with the physicochemical properties of reported multiple series PAMs. This comprehensive work sheds additional light on the binding mechanism and physicochemical regularity underlining PAMs affinity and could be further utilized as a structural and energetic blueprint for discovering and assessing novel PAMs for hmGlu2.
Collapse
Affiliation(s)
- Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Xiaonan Gao
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Ke Zhang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Qinglan Pei
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Xiaobo Xu
- 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.
| | - Chenxi Jing
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| |
Collapse
|
4
|
Erol I, Aksoydan B, Kantarcioglu I, Salmas RE, Durdagi S. Identification of novel serotonin reuptake inhibitors targeting central and allosteric binding sites: A virtual screening and molecular dynamics simulations study. J Mol Graph Model 2017; 74:193-202. [DOI: 10.1016/j.jmgm.2017.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 01/26/2017] [Accepted: 02/02/2017] [Indexed: 10/19/2022]
|
5
|
Discovery of novel dual VEGFR2 and Src inhibitors using a multistep virtual screening approach. Future Med Chem 2016; 9:7-24. [PMID: 27995811 DOI: 10.4155/fmc-2016-0162] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
AIM Simultaneous inhibition of VEGFR2 and Src may enhance the efficacy of VEGFR2-targeted cancer therapeutics. Hence, development of dual inhibitors on VEGFR2 and Src can be a useful strategy for such treatments. MATERIALS & METHODS A multistep virtual screening protocol, comprising ligand-based support vector machines method, drug-likeness rules filter and structure-based molecular docking, was developed and employed to identify dual inhibitors of VEGFR2 and Src from a large commercial chemical library. Kinase inhibitory assays and cell viability assays were then used for experimental validation. RESULTS A set of compounds belonging to six different molecular scaffolds was identified and sent for biological evaluation. Compound 3c belonging to the 2-amino-3-cyanopyridine scaffold exhibited good antiproliferative effect and dual-target activities against VEGFR2 and Src. CONCLUSION This study demonstrated the ability of the multistep virtual screening approach to identify novel multitarget agents.
Collapse
|
6
|
Cao GP, Thangapandian S, Son M, Kumar R, Choi YJ, Kim Y, Kwon YJ, Kim HH, Suh JK, Lee KW. QSAR modeling to design selective histone deacetylase 8 (HDAC8) inhibitors. Arch Pharm Res 2016; 39:1356-1369. [DOI: 10.1007/s12272-015-0705-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 12/31/2015] [Indexed: 12/28/2022]
|
7
|
Co-targeting cancer drug escape pathways confers clinical advantage for multi-target anticancer drugs. Pharmacol Res 2015; 102:123-31. [DOI: 10.1016/j.phrs.2015.09.019] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2015] [Revised: 09/24/2015] [Accepted: 09/29/2015] [Indexed: 02/07/2023]
|
8
|
Cao GP, Arooj M, Thangapandian S, Park C, Arulalapperumal V, Kim Y, Kwon YJ, Kim HH, Suh JK, Lee KW. A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:397-420. [PMID: 25986171 DOI: 10.1080/1062936x.2015.1040453] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure-activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Finally, HDAC8 structures in complex with five hits were also subjected to 5 ns molecular dynamics (MD) simulations to evaluate the complex structure stability. To the best of our knowledge, the NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery.
Collapse
Affiliation(s)
- G P Cao
- a Department of Biochemistry, Division of Applied Life Science (BK21 Plus Program) , Systems and Synthetic Agrobiotech Centre (SSAC), Plant Molecular Biology and Biotechnology Research Centre (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University , Jinju , Republic of Korea
| | | | | | | | | | | | | | | | | | | |
Collapse
|
9
|
Jain (Pancholi) N, Gupta S, Sapre N, Sapre NS. In silico de novo design of novel NNRTIs: a bio-molecular modelling approach. RSC Adv 2015. [DOI: 10.1039/c4ra15478a] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Six novel NNRTIs (DABO) with high efficacy are designed by assessing the interaction potential and structural requirements using chemometric analyses (SVM, BPNN and MLR) on structural descriptors.
Collapse
Affiliation(s)
| | - Swagata Gupta
- Department of Chemistry
- Govt. BLPPG College
- MHOW, India
| | - Neelima Sapre
- Department of Mathematics and Computational Sc
- SGSITS
- Indore, India
| | | |
Collapse
|
10
|
Jain Pancholi N, Gupta S, Sapre N, Sapre NS. Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1. MOLECULAR BIOSYSTEMS 2014; 10:313-25. [PMID: 24292893 DOI: 10.1039/c3mb70218a] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Researchers are on the constant lookout for new antiviral agents for the treatment of AIDS. In the present work, ligand based modeling studies are performed on analogues of substituted phenyl-thio-thymines, which act as non-nucleoside reverse transcriptase inhibitors (NNRTIs) and novel leads are extracted. Using alignment-dependent descriptors, based on group center overlap (SALL, HDALL, HAALL and RALL), an alignment-independent descriptor (S log P), a topological descriptor (Balaban index (J)) and a 3D descriptor dipole moment (μ) and shape based descriptors (Kappa 2 index ((2)κ)), a correlation is derived with inhibitory activity. Linear and non-linear techniques have been used to achieve the goal. Support Vector Machine (SVM, R = 0.929, R(2) = 0.863) and Back Propagation Neural Network (BPNN, R = 0.928, R(2) = 0.861) methods yielded near similar results and outperformed Multiple Linear Regression (MLR, R = 0.915, R(2) = 0.837). The predictive ability of the models are cross-validated using a test dataset (SVM: R = 0.846, R(2) = 0.716, BPNN: R = 0.841, R(2) = 0.707 and MLR: R = 0.833, R(2) = 0.694). It is concluded that the hydrophobicity (S log P) and the polarity (μ) of a ligand and the presence of hydrogen donor (HDALL) moieties are the deciding factors in improving antiviral activity and pharmaco-therapeutic properties. Based on the above findings, a virtual dataset is created to extract probable leads with reasonable antiviral activity as well as better pharmacophoric properties.
Collapse
Affiliation(s)
- Nilanjana Jain Pancholi
- Department of Applied Chemistry, Shri G.S. Institute of Technology and Sciences, Indore, MP 452001, India.
| | | | | | | |
Collapse
|
11
|
Shin JS, Ha JH, Chi SW. Targeting of p53 peptide analogues to anti-apoptotic Bcl-2 family proteins as revealed by NMR spectroscopy. Biochem Biophys Res Commun 2014; 443:882-7. [DOI: 10.1016/j.bbrc.2013.12.054] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2013] [Accepted: 12/10/2013] [Indexed: 10/25/2022]
|
12
|
Jin F, Gao D, Wu Q, Liu F, Chen Y, Tan C, Jiang Y. Exploration of N-(2-aminoethyl)piperidine-4-carboxamide as a potential scaffold for development of VEGFR-2, ERK-2 and Abl-1 multikinase inhibitor. Bioorg Med Chem 2013; 21:5694-706. [DOI: 10.1016/j.bmc.2013.07.026] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2013] [Revised: 07/11/2013] [Accepted: 07/11/2013] [Indexed: 01/09/2023]
|
13
|
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]
|
14
|
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.
Collapse
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)
| |
Collapse
|