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Wang K, Zhong F, Zhang ZD, Li HQ, Tian S. Recent advances in the development of P2Y 14R inhibitors: a patent and literature review (2018-present). Expert Opin Ther Pat 2024; 34:611-625. [PMID: 38889204 DOI: 10.1080/13543776.2024.2369634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024]
Abstract
INTRODUCTION The P2Y14 receptor (P2Y14R), a member of the G protein-coupled receptor family, is activated by extracellular nucleotides. Due to its involvement in inflammatory, immunological and other associated processes, P2Y14R has emerged as a promising therapeutic target. Despite lacking a determined three-dimensional crystal structure, the homology modeling technique based on closely related P2Y receptors' crystallography has been extensively utilized for developing active compounds targeting P2Y14R. Recent discoveries have unveiled numerous highly effective and subtype-specific P2Y14R inhibitors. This study presents an overview of the latest advancements in P2Y14R inhibitors. AREAS COVERED This review presents an overview of the advancements in P2Y14R inhibitor research over the past five years, encompassing new patents, journal articles, and highlighting the therapeutic prospects inherent in these compounds. EXPERT OPINION The recent revelation of the vast potential of P2Y14R inhibitors has led to the development of novel compounds that exhibit promising capabilities for the treatment of sterile inflammation of the kidney, potentially diabetes, and asthma. Despite being a relatively nascent class of compounds, certain members have already exhibited their capacity to surmount specific challenges posed by conventional P2Y14R inhibitors. Targeting P2Y14R through small molecules may present a promising therapeutic strategy for effectively managing diverse inflammatory diseases.
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Affiliation(s)
- Kai Wang
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Fen Zhong
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Zhou-Dong Zhang
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Huan-Qiu Li
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, China
| | - Sheng Tian
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, China
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2
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Yang ZY, Yang ZJ, Lu AP, Hou TJ, Cao DS. Scopy: an integrated negative design python library for desirable HTS/VS database design. Brief Bioinform 2020; 22:5901981. [PMID: 32892221 DOI: 10.1093/bib/bbaa194] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/27/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. CONCLUSION Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University (Changsha)
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, China
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3
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Nguyen-Vo TH, Nguyen L, Do N, Nguyen TN, Trinh K, Cao H, Le L. Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery. J Chem Inf Model 2020; 60:1101-1110. [PMID: 31873010 DOI: 10.1021/acs.jcim.9b00826] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.
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Affiliation(s)
- Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Loc Nguyen
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Nguyet Do
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Thien-Ngan Nguyen
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Khang Trinh
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Hung Cao
- The Henry Samueli School of Engineering, University of California at Irvine, Irvine, California 92697, United States
| | - Ly Le
- Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam.,Vingroup Big Data Institute, Ha Noi 100000, Vietnam
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4
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Yang ZY, He JH, Lu AP, Hou TJ, Cao DS. Application of Negative Design To Design a More Desirable Virtual Screening Library. J Med Chem 2020; 63:4411-4429. [DOI: 10.1021/acs.jmedchem.9b01476] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Jun-Hong He
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, P. R. China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, P. R. China
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5
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Scotti L, Monteiro AFM, de Oliveira Viana J, Mendonça Junior FJB, Ishiki HM, Tchouboun EN, Santos R, Scotti MT. Multi-Target Drugs Against Metabolic Disorders. Endocr Metab Immune Disord Drug Targets 2020; 19:402-418. [PMID: 30556507 DOI: 10.2174/1871530319666181217123357] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 06/18/2018] [Accepted: 06/27/2018] [Indexed: 01/13/2023]
Abstract
BACKGROUND Metabolic disorders are a major cause of illness and death worldwide. Metabolism is the process by which the body makes energy from proteins, carbohydrates, and fats; chemically breaking these down in the digestive system towards sugars and acids which constitute the human body's fuel for immediate use, or to store in body tissues, such as the liver, muscles, and body fat. OBJECTIVE The efficiency of treatments for multifactor diseases has not been proved. It is accepted that to manage multifactor diseases, simultaneous modulation of multiple targets is required leading to the development of new strategies for discovery and development of drugs against metabolic disorders. METHODS In silico studies are increasingly being applied by researchers due to reductions in time and costs for new prototype synthesis; obtaining substances that present better therapeutic profiles. DISCUSSION In the present work, in addition to discussing multi-target drug discovery and the contributions of in silico studies to rational bioactive planning against metabolic disorders such as diabetes and obesity, we review various in silico study contributions to the fight against human metabolic pathologies. CONCLUSION In this review, we have presented various studies involved in the treatment of metabolic disorders; attempting to obtain hybrid molecules with pharmacological activity against various targets and expanding biological activity by using different mechanisms of action to treat a single pathology.
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Affiliation(s)
- Luciana Scotti
- Teaching and Research Management - University Hospital, Federal University of Paraíba, João Pessoa, PB, Brazil.,Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, Joao Pessoa, PB, Brazil
| | - Alex France Messias Monteiro
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, Joao Pessoa, PB, Brazil
| | - Jéssika de Oliveira Viana
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, Joao Pessoa, PB, Brazil
| | - Francisco Jaime Bezerra Mendonça Junior
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, Joao Pessoa, PB, Brazil.,Laboratory of Synthesis and Drug Delivery, Department of Biological Science, State University of Paraiba, Joao Pessoa, PB, Brazil
| | - Hamilton M Ishiki
- University of Western Sao Paulo (Unoeste), Presidente Prudente, SP, Brazil
| | | | - Rodrigo Santos
- Laboratory of Synthesis and Drug Delivery, Department of Biological Science, State University of Paraiba, Joao Pessoa, PB, Brazil
| | - Marcus Tullius Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, Joao Pessoa, PB, Brazil
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6
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Yang H, Sun L, Wang Z, Li W, Liu G, Tang Y. ADMETopt: A Web Server for ADMET Optimization in Drug Design via Scaffold Hopping. J Chem Inf Model 2018; 58:2051-2056. [DOI: 10.1021/acs.jcim.8b00532] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Lixia Sun
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zhuang Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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7
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Shang J, Hu B, Wang J, Zhu F, Kang Y, Li D, Sun H, Kong DX, Hou T. Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products. J Chem Inf Model 2018; 58:1182-1193. [PMID: 29792805 DOI: 10.1021/acs.jcim.8b00125] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
This is a new golden age for drug discovery based on natural products derived from both marine and terrestrial sources. Herein, a straightforward but important question is "what are the major structural differences between marine natural products (MNPs) and terrestrial natural products (TNPs)?" To answer this question, we analyzed the important physicochemical properties, structural features, and drug-likeness of the two types of natural products and discussed their differences from the perspective of evolution. In general, MNPs have lower solubility and are often larger than TNPs. On average, particularly from the perspective of unique fragments and scaffolds, MNPs usually possess more long chains and large rings, especially 8- to 10-membered rings. MNPs also have more nitrogen atoms and halogens, notably bromines, and fewer oxygen atoms, suggesting that MNPs may be synthesized by more diverse biosynthetic pathways than TNPs. Analysis of the frequently occurring Murcko frameworks in MNPs and TNPS also reveals a striking difference between MNPs and TNPs. The scaffolds of the former tend to be longer and often contain ester bonds connected to 10-membered rings, while the scaffolds of the latter tend to be shorter and often bear more stable ring systems and bond types. Besides, the prediction from the naïve Bayesian drug-likeness classification model suggests that most compounds in MNPs and TNPs are drug-like, although MNPs are slightly more drug-like than TNPs. We believe that MNPs and TNPs with novel drug-like scaffolds have great potential to be drug leads or drug candidates in drug discovery campaigns.
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Affiliation(s)
- Jun Shang
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China.,State Key Laboratory of Agricultural Microbiology and Agricultural Bioinformatics, Key Laboratory of Hubei Province, College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China.,State Key Lab of CAD&CG , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Ben Hu
- State Key Laboratory of Agricultural Microbiology and Agricultural Bioinformatics, Key Laboratory of Hubei Province, College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China
| | - Junmei Wang
- Department of Pharmaceutical Sciences , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States
| | - Feng Zhu
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Yu Kang
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Dan Li
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Huiyong Sun
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - De-Xin Kong
- State Key Laboratory of Agricultural Microbiology and Agricultural Bioinformatics, Key Laboratory of Hubei Province, College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China
| | - Tingjun Hou
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China.,State Key Lab of CAD&CG , Zhejiang University , Hangzhou , Zhejiang 310058 , China
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8
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Feng T, Chen F, Kang Y, Sun H, Liu H, Li D, Zhu F, Hou T. HawkRank: a new scoring function for protein-protein docking based on weighted energy terms. J Cheminform 2017; 9:66. [PMID: 29282565 PMCID: PMC5745212 DOI: 10.1186/s13321-017-0254-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 12/14/2017] [Indexed: 01/09/2023] Open
Abstract
Deciphering the structural determinants of protein–protein interactions (PPIs) is essential to gain a deep understanding of many important biological functions in the living cells. Computational approaches for the structural modeling of PPIs, such as protein–protein docking, are quite needed to complement existing experimental techniques. The reliability of a protein–protein docking method is dependent on the ability of the scoring function to accurately distinguish the near-native binding structures from a huge number of decoys. In this study, we developed HawkRank, a novel scoring function designed for the sampling stage of protein–protein docking by summing the contributions from several energy terms, including van der Waals potentials, electrostatic potentials and desolvation potentials. First, based on the solvation free energies predicted by the Generalized Born model for ~ 800 proteins, a SASA (solvent accessible surface area)-based solvation model was developed, which can give the aqueous solvation free energies for proteins by summing the contributions of 21 atom types. Then, the van der Waals potentials and electrostatic potentials based on the Amber ff14SB force field were computed. Finally, the HawkRank scoring function was derived by determining the most optimal weights for five energy terms based on the training set. Here, MSR (modified success rate), a novel protein–protein scoring quality index, was used to assess the performance of HawkRank and three other popular protein–protein scoring functions, including ZRANK, FireDock and dDFIRE. The results show that HawkRank outperformed the other three scoring functions according to the total number of hits and MSR. HawkRank is available at http://cadd.zju.edu.cn/programs/hawkrank.
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Affiliation(s)
- Ting Feng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Fu Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Huiyong Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Hui Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China. .,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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9
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Lei T, Chen F, Liu H, Sun H, Kang Y, Li D, Li Y, Hou T. ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity. Mol Pharm 2017; 14:2407-2421. [PMID: 28595388 DOI: 10.1021/acs.molpharmaceut.7b00317] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (q2ext = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity.
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Affiliation(s)
- Tailong Lei
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Fu Chen
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Hui Liu
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Huiyong Sun
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University , Suzhou, Jiangsu 215123, P. R. China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China.,State Key Lab of CAD&CG, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
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10
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Zhan Z, Li L, Tian S, Zhen X, Li Y. Prediction of chemical biodegradability using computational methods. MOLECULAR SIMULATION 2017. [DOI: 10.1080/08927022.2017.1328556] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Zhixiong Zhan
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, P.R. China
| | - Linlang Li
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, P.R. China
| | - Sheng Tian
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, P.R. China
| | - Xuechu Zhen
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, P.R. China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, P.R. China
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11
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Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2017; 2017:7198645. [PMID: 28690664 PMCID: PMC5485337 DOI: 10.1155/2017/7198645] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Accepted: 05/15/2017] [Indexed: 12/25/2022]
Abstract
With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.
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12
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Shang J, Sun H, Liu H, Chen F, Tian S, Pan P, Li D, Kong D, Hou T. Comparative analyses of structural features and scaffold diversity for purchasable compound libraries. J Cheminform 2017; 9:25. [PMID: 29086044 PMCID: PMC5400773 DOI: 10.1186/s13321-017-0212-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 04/09/2017] [Indexed: 11/30/2022] Open
Abstract
Large purchasable screening libraries of small molecules afforded by commercial vendors are indispensable sources for virtual screening (VS). Selecting an optimal screening library for a specific VS campaign is quite important to improve the success rates and avoid wasting resources in later experimental phases. Analysis of the structural features and molecular diversity for different screening libraries can provide valuable information to the decision making process when selecting screening libraries for VS. In this study, the structural features and scaffold diversity of eleven purchasable screening libraries and Traditional Chinese Medicine Compound Database (TCMCD) were analyzed and compared. Their scaffold diversity represented by the Murcko frameworks and Level 1 scaffolds was characterized by the scaffold counts and cumulative scaffold frequency plots, and visualized by Tree Maps and SAR Maps. The analysis demonstrates that, based on the standardized subsets with similar molecular weight distributions, Chembridge, ChemicalBlock, Mucle, TCMCD and VitasM are more structurally diverse than the others. Compared with all purchasable screening libraries, TCMCD has the highest structural complexity indeed but more conservative molecular scaffolds. Moreover, we found that some representative scaffolds were important components of drug candidates against different drug targets, such as kinases and guanosine-binding protein coupled receptors, and therefore the molecules containing pharmacologically important scaffolds found in screening libraries might be potential inhibitors against the relevant targets. This study may provide valuable perspective on which purchasable compound libraries are better for you to screen.Selecting diverse compound libraries with scaffold analyses. ![]()
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Affiliation(s)
- Jun Shang
- State Key Laboratory of Agricultural Microbiology and Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Hubei, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Huiyong Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Hui Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Fu Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Sheng Tian
- College of Pharmaceutical Sciences, Soochow University, Suzhou, 215021, Jiangsu, China
| | - Peichen Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Dexin Kong
- State Key Laboratory of Agricultural Microbiology and Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Hubei, China.
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China. .,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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13
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Fu X, Mervin LH, Li X, Yu H, Li J, Mohamad Zobir SZ, Zoufir A, Zhou Y, Song Y, Wang Z, Bender A. Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis. J Chem Inf Model 2017; 57:468-483. [DOI: 10.1021/acs.jcim.6b00725] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xianjun Fu
- School
of Information Management, Shandong University of Traditional Chinese Medicine, 250355 Jinan, China
- Centre
for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Lewis H. Mervin
- Centre
for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Xuebo Li
- School
of Information Management, Shandong University of Traditional Chinese Medicine, 250355 Jinan, China
| | - Huayun Yu
- College
of TCM, Shandong University of Traditional Chinese Medicine, 250355 Jinan, China
| | - Jiaoyang Li
- School
of Information Management, Shandong University of Traditional Chinese Medicine, 250355 Jinan, China
| | - Siti Zuraidah Mohamad Zobir
- Centre
for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Azedine Zoufir
- Centre
for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Yang Zhou
- School
of Information Management, Shandong University of Traditional Chinese Medicine, 250355 Jinan, China
| | - Yongmei Song
- School
of Information Management, Shandong University of Traditional Chinese Medicine, 250355 Jinan, China
| | - Zhenguo Wang
- School
of Information Management, Shandong University of Traditional Chinese Medicine, 250355 Jinan, China
| | - Andreas Bender
- Centre
for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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14
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Zhang SN, Li XZ, Yang XY. Drug-likeness prediction of chemical constituents isolated from Chinese materia medica Ciwujia. JOURNAL OF ETHNOPHARMACOLOGY 2017; 198:131-138. [PMID: 28065780 DOI: 10.1016/j.jep.2017.01.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 12/10/2016] [Accepted: 01/04/2017] [Indexed: 06/06/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Ciwujia (CWJ), one of the most commonly used Chinese materia medicas (CMMs), is derived from the roots, rhizomes, and stems of Acanthopanax senticosus harms (AS). CWJ has been used for the treatment of various central nervous system (CNS) and peripheral system diseases. Drug-likeness prediction can help to analyze the absorption, distribution, metabolism, and excretion (ADME) processes of the compounds in CWJ, as well as their potential therapeutic and toxic effects, which is of significance in the confirmation of the active material bases of CWJ. MATERIALS AND METHODS The ADME properties of the compounds were calculated through web based PreADMET program and ACD/I-Lab 2.0. The potential therapeutic and toxicity targets of these compounds were screened by the ChemQuery tool in DrugBank and T3DB. RESULTS 14/39 compounds had moderate or good oral bioavailability (OB). 29/39 compounds bound weakly to the plasma proteins. 18/39 compounds might pass across the blood-brain barrier (BBB). Most of these compounds showed low renal excretion ability. 25/39 compounds had 99 structurally similar drugs and 158 potential therapeutic targets. Additionally, 17/39 compounds had 53 structurally similar toxins and 126 potential toxicity targets. CONCLUSION Our study suggests that these compounds have a certain drug-likeness potentials, which are also likely to be the material bases of CWJ. These results may provide a reference for the safe use of CWJ and the expansion of its application scope.
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Affiliation(s)
- Shuai-Nan Zhang
- Department of Pharmacy, Guiyang University of Chinese Medicine, Guiyang 550025, PR China
| | - Xu-Zhao Li
- Department of Pharmacy, Guiyang University of Chinese Medicine, Guiyang 550025, PR China.
| | - Xu-Yan Yang
- First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin 150040, PR China.
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15
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Lei T, Li Y, Song Y, Li D, Sun H, Hou T. ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. J Cheminform 2016; 8:6. [PMID: 26839598 PMCID: PMC4736633 DOI: 10.1186/s13321-016-0117-7] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 01/20/2016] [Indexed: 01/31/2023] Open
Abstract
Background
Determination of acute toxicity, expressed as median lethal dose (LD50), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity.
Results In this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD50 values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by qext2 for the test set containing 2376 molecules, ranged from 0.572 to 0.659. Conclusion Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (qext2 = 0.669–0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity.
Workflow of combinatorial QSAR modelling to predict rat oral acute toxicity ![]()
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Affiliation(s)
- Tailong Lei
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang People's Republic of China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, 215123 Jiangsu People's Republic of China
| | - Yunlong Song
- Department of Medicinal Chemistry, School of Pharmacy, Second Military Medical University, Shanghai, 200433 People's Republic of China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang People's Republic of China
| | - Huiyong Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang People's Republic of China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang People's Republic of China ; State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang People's Republic of China
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16
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Exploration of Scaffolds from Natural Products with Antiplasmodial Activities, Currently Registered Antimalarial Drugs and Public Malarial Screen Data. Molecules 2016; 21:104. [PMID: 26784165 PMCID: PMC6273396 DOI: 10.3390/molecules21010104] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 01/06/2016] [Accepted: 01/12/2016] [Indexed: 01/07/2023] Open
Abstract
In light of current resistance to antimalarial drugs, there is a need to discover new classes of antimalarial agents with unique mechanisms of action. Identification of unique scaffolds from natural products with in vitro antiplasmodial activities may be the starting point for such new classes of antimalarial agents. We therefore conducted scaffold diversity and comparison analysis of natural products with in vitro antiplasmodial activities (NAA), currently registered antimalarial drugs (CRAD) and malaria screen data from Medicine for Malaria Ventures (MMV). The scaffold diversity analyses on the three datasets were performed using scaffold counts and cumulative scaffold frequency plots. Scaffolds from the NAA were compared to those from CRAD and MMV. A Scaffold Tree was also generated for each of the datasets and the scaffold diversity of NAA was found to be higher than that of MMV. Among the NAA compounds, we identified unique scaffolds that were not contained in any of the other compound datasets. These scaffolds from NAA also possess desirable drug-like properties making them ideal starting points for antimalarial drug design considerations. The Scaffold Tree showed the preponderance of ring systems in NAA and identified virtual scaffolds, which may be potential bioactive compounds.
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17
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Zhang Y, Xu L, Zhang Z, Zhang Z, Zheng L, Li D, Li Y, Liu F, Yu K, Hou T, Zhen X. Structure-Activity Relationships and Anti-inflammatory Activities of N-Carbamothioylformamide Analogues as MIF Tautomerase Inhibitors. J Chem Inf Model 2015; 55:1994-2004. [PMID: 26288111 DOI: 10.1021/acs.jcim.5b00445] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Macrophage migration inhibitory factor (MIF), a proinflammatory cytokine, is an attractive therapeutic target for the treatment of inflammatory diseases. In our previous study, 3-[(biphenyl-4-ylcarbonyl)carbamothioyl]amino benzoic acid (compound 1) was discovered as a potent inhibitor of MIF by docking-based virtual screening and bioassays. Here, a series of analogues of compound 1 derived from similarity search and chemical synthesis were evaluated for their MIF tautomerase activities, and their structure-activity relationships were then analyzed. The most potent inhibitor (compound 5) with an IC50 of 370 nM strongly suppressed lipopolysaccharide (LPS)-induced production of TNF-α and IL-6 in a dose-dependent manner and significantly enhanced the survival rate of mice with LPS-induced endotoxic shock from 0 to 35% at 0.5 mg/kg and to 45% at 1 mg/kg, highlighting the therapeutic potential of the MIF tautomerase inhibition in inflammatory diseases.
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Affiliation(s)
- Yu Zhang
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases & Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University , Suzhou, Jiangsu 215123, China
| | - Lei Xu
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.,Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology , Changzhou 213001, China
| | - Zhiqiang Zhang
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases & Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University , Suzhou, Jiangsu 215123, China
| | - Zhiyu Zhang
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases & Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University , Suzhou, Jiangsu 215123, China
| | - Longtai Zheng
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases & Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University , Suzhou, Jiangsu 215123, China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University , Suzhou, Jiangsu 215123, China
| | - Feng Liu
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases & Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University , Suzhou, Jiangsu 215123, China
| | - Kunqian Yu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 200031, China
| | - Tingjun Hou
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases & Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University , Suzhou, Jiangsu 215123, China.,College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.,Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University , Suzhou, Jiangsu 215123, China
| | - Xuechu Zhen
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases & Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University , Suzhou, Jiangsu 215123, China
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18
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Tian S, Wang J, Li Y, Li D, Xu L, Hou T. The application of in silico drug-likeness predictions in pharmaceutical research. Adv Drug Deliv Rev 2015; 86:2-10. [PMID: 25666163 DOI: 10.1016/j.addr.2015.01.009] [Citation(s) in RCA: 241] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Revised: 01/14/2015] [Accepted: 01/29/2015] [Indexed: 02/08/2023]
Abstract
The concept of drug-likeness, established from the analyses of the physiochemical properties or/and structural features of existing small organic drugs or/and drug candidates, has been widely used to filter out compounds with undesirable properties, especially poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Here, we summarize various approaches for drug-likeness evaluations, including simple rules/filters based on molecular properties/structures and quantitative prediction models based on sophisticated machine learning methods, and provide a comprehensive review of recent advances in this field. Moreover, the strengths and weaknesses of these approaches are briefly outlined. Finally, the drug-likeness analyses of natural products and traditional Chinese medicines (TCM) are discussed.
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Affiliation(s)
- Sheng Tian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China
| | - Junmei Wang
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Lei Xu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China.
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19
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Pascolutti M, Campitelli M, Nguyen B, Pham N, Gorse AD, Quinn RJ. Capturing nature's diversity. PLoS One 2015; 10:e0120942. [PMID: 25902039 PMCID: PMC4406718 DOI: 10.1371/journal.pone.0120942] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Accepted: 02/09/2015] [Indexed: 12/15/2022] Open
Abstract
Natural products are universally recognized to contribute valuable chemical diversity to the design of molecular screening libraries. The analysis undertaken in this work, provides a foundation for the generation of fragment screening libraries that capture the diverse range of molecular recognition building blocks embedded within natural products. Physicochemical properties were used to select fragment-sized natural products from a database of known natural products (Dictionary of Natural Products). PCA analysis was used to illustrate the positioning of the fragment subset within the property space of the non-fragment sized natural products in the dataset. Structural diversity was analysed by three distinct methods: atom function analysis, using pharmacophore fingerprints, atom type analysis, using radial fingerprints, and scaffold analysis. Small pharmacophore triplets, representing the range of chemical features present in natural products that are capable of engaging in molecular interactions with small, contiguous areas of protein binding surfaces, were analysed. We demonstrate that fragment-sized natural products capture more than half of the small pharmacophore triplet diversity observed in non fragment-sized natural product datasets. Atom type analysis using radial fingerprints was represented by a self-organizing map. We examined the structural diversity of non-flat fragment-sized natural product scaffolds, rich in sp3 configured centres. From these results we demonstrate that 2-ring fragment-sized natural products effectively balance the opposing characteristics of minimal complexity and broad structural diversity when compared to the larger, more complex fragment-like natural products. These naturally-derived fragments could be used as the starting point for the generation of a highly diverse library with the scope for further medicinal chemistry elaboration due to their minimal structural complexity. This study highlights the possibility to capture a high proportion of the individual molecular interaction motifs embedded within natural products using a fragment screening library spanning 422 structural clusters and comprised of approximately 2800 natural products.
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Affiliation(s)
- Mauro Pascolutti
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
| | - Marc Campitelli
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
| | - Bao Nguyen
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
| | - Ngoc Pham
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
| | - Alain-Dominique Gorse
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
- QFAB Bioinformatics, The University of Queensland, Brisbane, Queensland, Australia
| | - Ronald J. Quinn
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
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20
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Korkmaz S, Zararsiz G, Goksuluk D. Drug/nondrug classification using Support Vector Machines with various feature selection strategies. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:51-60. [PMID: 25224081 DOI: 10.1016/j.cmpb.2014.08.009] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 08/15/2014] [Accepted: 08/27/2014] [Indexed: 06/03/2023]
Abstract
In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM-Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.
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Affiliation(s)
- Selcuk Korkmaz
- Hacettepe University, Faculty of Medicine, Department of Biostatistics, 06100 Sihhiye, Ankara, Turkey.
| | - Gokmen Zararsiz
- Hacettepe University, Faculty of Medicine, Department of Biostatistics, 06100 Sihhiye, Ankara, Turkey
| | - Dincer Goksuluk
- Hacettepe University, Faculty of Medicine, Department of Biostatistics, 06100 Sihhiye, Ankara, Turkey
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21
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Arya H, Coumar MS. Virtual screening of traditional Chinese medicine (TCM) database: identification of fragment-like lead molecules for filariasis target asparaginyl-tRNA synthetase. J Mol Model 2014; 20:2266. [PMID: 24842326 DOI: 10.1007/s00894-014-2266-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2014] [Accepted: 04/23/2014] [Indexed: 12/28/2022]
Abstract
Lymphatic filariasis (LF) is a vector borne infectious disease caused by the nematode Wuchereria bancrofti, Brugia malayi, and Brugia timori. Over 120 million people are affected by LF in the world, of which two-thirds are in Asia. The infection restricts the normal flow of lymph from the infected area resulting in swelling of the extremities and causing permanent disability. As the available drugs for the treatment of LF are becoming ineffective due to the development of resistance, there is an urgent need to find new leads for drug development. In this study, asparaginyl-tRNA synthetase (AsnRS; PDB ID: 2XGT) essential for the protein bio-synthesis in the filarial nematode was used to carry out virtual screening (VS) of plant constituents from traditional Chinese medicine (TCM) database. Docking as well as E-pharmacophore based VS were carried out to identify the hits. The top scoring hits, Agri 1 (1,3,8-trihydroxy-4,5-dimethoxyxanthen-9-one-3-O-beta-D-glucopyranoside) and Agri 2 (5,7-dihydroxy-2-propylchromone 7-O-beta-D-glucopyranoside), constituents of Agrimonia pilosa, were selected for molecular dynamics (MD) simulation study for 10 ns. MD simulation showed that both the glycosides Agri 1 and Agri 2 were forming stable interactions with the target protein. Moreover, docking and MD simulation of the lead A (1,3,8-trihydroxy-4,5-dimethoxyxanthen-9-one; Mol. Wt.: 304.25; CLogP: 3.07) and lead B (5,7-dihydroxy-2-propylchromone; Mol. Wt.: 220.22; CLogP: 3.02), the aglycones of Agri 1 and Agri 2, respectively, were carried out with the target AsnRS. The in silico investigations of the aglycones suggest that the lead B could be a suitable fragment-like lead molecule for anti-filarial drug discovery.
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Affiliation(s)
- Hemant Arya
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, 605014, India
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22
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Gu J, Chen L, Yuan G, Xu X. A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2013; 2013:203614. [PMID: 24223610 PMCID: PMC3810496 DOI: 10.1155/2013/203614] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2013] [Accepted: 09/19/2013] [Indexed: 12/29/2022]
Abstract
The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive.
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Affiliation(s)
- Jiangyong Gu
- Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Lirong Chen
- Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Gu Yuan
- Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Xiaojie Xu
- Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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23
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Tian S, Li Y, Li D, Xu X, Wang J, Zhang Q, Hou T. Modeling Compound–Target Interaction Network of Traditional Chinese Medicines for Type II Diabetes Mellitus: Insight for Polypharmacology and Drug Design. J Chem Inf Model 2013; 53:1787-803. [DOI: 10.1021/ci400146u] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sheng Tian
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xiaojie Xu
- College of Chemistry and Molecular
Engineering, Peking University, Beijing
100871, China
| | - Junmei Wang
- Department
of Biochemistry, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas,
Texas 75390, United States
| | - Qian Zhang
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Tingjun Hou
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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