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Li X, Li X, Li Y, Yu C, Xue W, Hu J, Li B, Wang P, Zhu F. What Makes Species Productive of Anti-Cancer Drugs? Clues from Drugs’ Species Origin, Druglikeness, Target and Pathway. Anticancer Agents Med Chem 2019; 19:194-203. [DOI: 10.2174/1871520618666181029132017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Revised: 08/22/2017] [Accepted: 03/21/2018] [Indexed: 12/18/2022]
Abstract
Background:Despite the substantial contribution of natural products to the FDA drug approval list, the discovery of anti-cancer drugs from the huge amount of species on the planet remains looking for a needle in a haystack. Objective: Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.Objective:Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.Methods:In this study, 260 anti-cancer drugs approved in the past 70 years were comprehensively analyzed by hierarchical clustering of phylogenetic distribution.Results:207 out of these 260 drugs were derived from or inspired by the natural products isolated from 58 species. Phylogenetic distribution of those drugs further revealed that nature-derived anti-cancer drugs originated mostly from drug-productive families that tend to be clustered rather than scattered on the phylogenetic tree. Moreover, based on their productivity, drug-producing species were categorized into productive (CPS), newly emerging (CNS) and lessproductive (CLS). Statistical significances in druglikeness between drugs from CPS and CLS were observed, and drugs from CNS were found to share similar drug-like properties to those from CPS.Conclusion:This finding indicated a great raise in drug approval standard, which suggested us to focus bioprospecting on the species yielding multiple drugs and keeping productive for long period of time.
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Affiliation(s)
- Xiaofeng Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xiaoxu Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yinghong Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Chunyan Yu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Weiwei Xue
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jie Hu
- School of International Studies, Zhejiang University, Hangzhou 310058, China
| | - Bo Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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Wang MY, Wang F, Hao GF, Yang GF. FungiPAD: A Free Web Tool for Compound Property Evaluation and Fungicide-Likeness Analysis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2019; 67:1823-1830. [PMID: 30677302 DOI: 10.1021/acs.jafc.8b06596] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The increasing prevalence of fungal diseases, continual development of resistance, and stringent environmental regulations have revealed an urgent need to develop more selective, safer, resistance-breaking, and cost-effective fungicides. However, most new fungicidal lead compounds fail in their late stages of development as a result of poor solubility or permeability, meaning that they have suboptimal physicochemical properties. Hence, the exploration of advanced technologies for compound "fungicide-likeness" assessment might overcome these obstacles and bring more chemical entities to market. FungiPAD ( http://chemyang.ccnu.edu.cn/ccb/database/FungiPAD/ ) is a free platform employed to predict physicochemical properties, bioavailability, and fungicide-likeness swiftly and powerfully using comprehensive approaches, such as physicochemical radars and qualitative and quantitative analyses. This platform contains data for over 16 000 physicochemical descriptors and the results of 2200 qualitative and 1100 quantitative analyses of marketed fungicides and provides comprehensive fungicide-likeness analysis for different compounds. The user-friendly interface facilitates interpretation and manipulation by non-computational scientists in support of fungicide discovery.
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Affiliation(s)
| | | | - Ge-Fei Hao
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals , Guizhou University , Guiyang , Guizhou 550025 , People's Republic of China
| | - Guang-Fu Yang
- Collaborative Innovation Center of Chemical Science and Engineering , Tianjin 300072 , People's Republic of China
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A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data. Future Med Chem 2018; 10:2641-2658. [PMID: 30499744 DOI: 10.4155/fmc-2018-0076] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time. In this review, we discuss a representative set of online virtual screening servers and their underlying similarity algorithms. Other related topics, such as molecular representation or freely accessible databases are also treated. The most relevant contributions to this review arise from critical discussions concerning the pros and cons of servers and algorithms, and the challenges that future works must solve in a virtual screening framework.
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Nath A, Kumari P, Chaube R. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives. Methods Mol Biol 2018; 1762:21-30. [PMID: 29594765 DOI: 10.1007/978-1-4939-7756-7_2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.
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Affiliation(s)
- Abhigyan Nath
- Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Priyanka Kumari
- Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Radha Chaube
- Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
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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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6
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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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Venkatraman V, Alsberg BK. KRAKENX: software for the generation of alignment-independent 3D descriptors. J Mol Model 2016; 22:93. [DOI: 10.1007/s00894-016-2957-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Accepted: 03/07/2016] [Indexed: 10/22/2022]
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8
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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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Ruiz-Blanco YB, Paz W, Green J, Marrero-Ponce Y. ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins. BMC Bioinformatics 2015; 16:162. [PMID: 25982853 PMCID: PMC4432771 DOI: 10.1186/s12859-015-0586-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 04/22/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The exponential growth of protein structural and sequence databases is enabling multifaceted approaches to understanding the long sought sequence-structure-function relationship. Advances in computation now make it possible to apply well-established data mining and pattern recognition techniques to these data to learn models that effectively relate structure and function. However, extracting meaningful numerical descriptors of protein sequence and structure is a key issue that requires an efficient and widely available solution. RESULTS We here introduce ProtDCal, a new computational software suite capable of generating tens of thousands of features considering both sequence-based and 3D-structural descriptors. We demonstrate, by means of principle component analysis and Shannon entropy tests, how ProtDCal's sequence-based descriptors provide new and more relevant information not encoded by currently available servers for sequence-based protein feature generation. The wide diversity of the 3D-structure-based features generated by ProtDCal is shown to provide additional complementary information and effectively completes its general protein encoding capability. As demonstration of the utility of ProtDCal's features, prediction models of N-linked glycosylation sites are trained and evaluated. Classification performance compares favourably with that of contemporary predictors of N-linked glycosylation sites, in spite of not using domain-specific features as input information. CONCLUSIONS ProtDCal provides a friendly and cross-platform graphical user interface, developed in the Java programming language and is freely available at: http://bioinf.sce.carleton.ca/ProtDCal/ . ProtDCal introduces local and group-based encoding which enhances the diversity of the information captured by the computed features. Furthermore, we have shown that adding structure-based descriptors contributes non-redundant additional information to the features-based characterization of polypeptide systems. This software is intended to provide a useful tool for general-purpose encoding of protein sequences and structures for applications is protein classification, similarity analyses and function prediction.
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Affiliation(s)
- Yasser B Ruiz-Blanco
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central "Marta Abreu" de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. .,Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
| | - Waldo Paz
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central "Marta Abreu" de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. .,Centre of Informatics Studies (CEI), Universidad Central "Marta Abreu" de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP:54830, Villa Clara, Cuba.
| | - James Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
| | - Yovani Marrero-Ponce
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central "Marta Abreu" de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. .,Grupo de Investigación Microbiología y Ambiente (GIMA). Programa de Bacteriología, Facultad Ciencias de la Salud, Universidad de San Buenaventura, Calle Real de Ternera, Cartagena (Bolivar), Colombia.
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Tao L, Zhu F, Qin C, Zhang C, Chen S, Zhang P, Zhang C, Tan C, Gao C, Chen Z, Jiang Y, Chen YZ. Clustered distribution of natural product leads of drugs in the chemical space as influenced by the privileged target-sites. Sci Rep 2015; 5:9325. [PMID: 25790752 PMCID: PMC5380136 DOI: 10.1038/srep09325] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Accepted: 02/18/2015] [Indexed: 01/02/2023] Open
Abstract
Some natural product leads of drugs (NPLDs) have been found to congregate in the chemical space. The extent, detailed patterns, and mechanisms of this congregation phenomenon have not been fully investigated and their usefulness for NPLD discovery needs to be more extensively tested. In this work, we generated and evaluated the distribution patterns of 442 NPLDs of 749 pre-2013 approved and 263 clinical trial small molecule drugs in the chemical space represented by the molecular scaffold and fingerprint trees of 137,836 non-redundant natural products. In the molecular scaffold trees, 62.7% approved and 37.4% clinical trial NPLDs congregate in 62 drug-productive scaffolds/scaffold-branches. In the molecular fingerprint tree, 82.5% approved and 63.0% clinical trial NPLDs are clustered in 60 drug-productive clusters (DCs) partly due to their preferential binding to 45 privileged target-site classes. The distribution patterns of the NPLDs are distinguished from those of the bioactive natural products. 11.7% of the NPLDs in these DCs have remote-similarity relationship with the nearest NPLD in their own DC. The majority of the new NPLDs emerge from preexisting DCs. The usefulness of the derived knowledge for NPLD discovery was demonstrated by the recognition of the new NPLDs of 2013-2014 approved drugs.
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Affiliation(s)
- Lin Tao
- 1] Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China [2] Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543 [3] NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456
| | - Feng Zhu
- 1] Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China [2] Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543 [3] Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, P. R. China
| | - Chu Qin
- 1] Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543 [2] NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456
| | - Cheng Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543
| | - Shangying Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543
| | - Peng Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543
| | - Cunlong Zhang
- Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China
| | - Chunyan Tan
- Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China
| | - Chunmei Gao
- Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China
| | - Zhe Chen
- Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, Hangzhou, P. R. China
| | - Yuyang Jiang
- Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China
| | - Yu Zong Chen
- 1] Department of Pharmacology and Pharmaceutical Sciences, School of Medicine, Tsinghua University, the Ministry-Province Jointly Constructed Base for State Key Lab-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, and Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, PO Box 518000, P. R. China [2] Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543 [3] NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456
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Zhao C, Zhang Y, Zou P, Wang J, He W, Shi D, Li H, Liang G, Yang S. Synthesis and biological evaluation of a novel class of curcumin analogs as anti-inflammatory agents for prevention and treatment of sepsis in mouse model. DRUG DESIGN DEVELOPMENT AND THERAPY 2015; 9:1663-78. [PMID: 25834403 PMCID: PMC4370917 DOI: 10.2147/dddt.s75862] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
A novel class of asymmetric mono-carbonyl analogs of curcumin (AMACs) were synthesized and screened for anti-inflammatory activity. These analogs are chemically stable as characterized by UV absorption spectra. In vitro, compounds 3f, 3m, 4b, and 4d markedly inhibited lipopolysaccharide (LPS)-induced expression of pro-inflammatory cytokines tumor necrosis factor-α and interleukin-6 in a dose-dependent manner, with IC50 values in low micromolar range. In vivo, compound 3f demonstrated potent preventive and therapeutic effects on LPS-induced sepsis in mouse model. Compound 3f downregulated the phosphorylation of extracellular signal-regulated kinase (ERK)1/2 MAPK and suppressed IκBα degradation, which suggests that the possible anti-inflammatory mechanism of compound 3f may be through downregulating nuclear factor kappa binding (NF-κB) and ERK pathways. Also, we solved the crystal structure of compound 3e to confirm the asymmetrical structure. The quantitative structure–activity relationship analysis reveals that the electron-withdrawing substituents on aromatic ring of lead structures could improve activity. These active AMACs represent a new class of anti-inflammatory agents with improved stability, bioavailability, and potency compared to curcumin. Our results suggest that 3f may be further developed as a potential agent for prevention and treatment of sepsis or other inflammation-related diseases.
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Affiliation(s)
- Chengguang Zhao
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, People's Republic of China ; Chemical Biology Research Center, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Yali Zhang
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, People's Republic of China ; Chemical Biology Research Center, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Peng Zou
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, People's Republic of China
| | - Jian Wang
- Department of Orthopedics, The 1st Affiliated Hospital, Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Wenfei He
- Chemical Biology Research Center, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Dengjian Shi
- Chemical Biology Research Center, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Huameng Li
- Chemical Biology Research Center, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Guang Liang
- Chemical Biology Research Center, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Shulin Yang
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, People's Republic of China
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12
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Zhang Y, Zhao L, Wu J, Jiang X, Dong L, Xu F, Zou P, Dai Y, Shan X, Yang S, Liang G. Synthesis and evaluation of a series of novel asymmetrical curcumin analogs for the treatment of inflammation. Molecules 2014; 19:7287-307. [PMID: 24901832 PMCID: PMC6271832 DOI: 10.3390/molecules19067287] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2014] [Revised: 05/11/2014] [Accepted: 05/12/2014] [Indexed: 11/28/2022] Open
Abstract
Curcumin has been reported to possess multiple bioactivities, such as antioxidant, anticancer, and anti-inflammatory properties, however the clinical application of curcumin has been significantly limited by its instability and poor metabolism. Modification of curcumin has led to discovery and development of lots of novel therapeutic candidates. In recent years acute and chronic inflammation has been the focus of numerous studies in various diseases. Here, we synthesized a series of asymmetrical curcumin analogs with high in vitro chemical stability, and their anti-inflammatory activity was evaluated in LPS-stimulated macrophages. According to the bio-screening results and QSAR analysis, these analogs exhibited potent activities against LPS-induced TNF-α and IL-6 release. Among the analogs of the potent anti-inflammatory activity, compounds 3b8 and 3b9 exhibited significant protection and possess enhanced anti-inflammatory activity thereby attenuated the LPS-induced septic death in mice.
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Affiliation(s)
- Yali Zhang
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China.
| | - Leping Zhao
- Department of Pharmacy at the Affiliated Yueqing Hospital, Wenzhou Medical University, Wenzhou 325699, Zhejiang, China.
| | - Jianzhang Wu
- Chemical Biology Research Center at School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China.
| | - Xin Jiang
- Chemical Biology Research Center at School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China.
| | - Lili Dong
- The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China.
| | - Fengli Xu
- The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China.
| | - Peng Zou
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China.
| | - Yuanrong Dai
- The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China.
| | - Xiaoou Shan
- The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China.
| | - Shulin Yang
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China.
| | - Guang Liang
- Chemical Biology Research Center at School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China.
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García-Jacas CR, Marrero-Ponce Y, Acevedo-Martínez L, Barigye SJ, Valdés-Martiní JR, Contreras-Torres E. QuBiLS-MIDAS: a parallel free-software for molecular descriptors computation based on multilinear algebraic maps. J Comput Chem 2014; 35:1395-409. [PMID: 24889018 DOI: 10.1002/jcc.23640] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Revised: 04/22/2014] [Accepted: 04/23/2014] [Indexed: 11/12/2022]
Abstract
The present report introduces the QuBiLS-MIDAS software belonging to the ToMoCoMD-CARDD suite for the calculation of three-dimensional molecular descriptors (MDs) based on the two-linear (bilinear), three-linear, and four-linear (multilinear or N-linear) algebraic forms. Thus, it is unique software that computes these tensor-based indices. These descriptors, establish relations for two, three, and four atoms by using several (dis-)similarity metrics or multimetrics, matrix transformations, cutoffs, local calculations and aggregation operators. The theoretical background of these N-linear indices is also presented. The QuBiLS-MIDAS software was developed in the Java programming language and employs the Chemical Development Kit library for the manipulation of the chemical structures and the calculation of the atomic properties. This software is composed by a desktop user-friendly interface and an Abstract Programming Interface library. The former was created to simplify the configuration of the different options of the MDs, whereas the library was designed to allow its easy integration to other software for chemoinformatics applications. This program provides functionalities for data cleaning tasks and for batch processing of the molecular indices. In addition, it offers parallel calculation of the MDs through the use of all available processors in current computers. The studies of complexity of the main algorithms demonstrate that these were efficiently implemented with respect to their trivial implementation. Lastly, the performance tests reveal that this software has a suitable behavior when the amount of processors is increased. Therefore, the QuBiLS-MIDAS software constitutes a useful application for the computation of the molecular indices based on N-linear algebraic maps and it can be used freely to perform chemoinformatics studies.
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Affiliation(s)
- César R García-Jacas
- Grupo de Investigación de Bioinformática, Centro de Estudio de Matemática Computacional, Universidad de las Ciencias Informáticas, La Habana, Cuba; Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central "Martha Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
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Zhang HP, Pan JB, Zhang C, Ji N, Wang H, Ji ZL. Network understanding of herb medicine via rapid identification of ingredient-target interactions. Sci Rep 2014; 4:3719. [PMID: 24429698 PMCID: PMC3893644 DOI: 10.1038/srep03719] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 12/19/2013] [Indexed: 01/03/2023] Open
Abstract
Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power.
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Affiliation(s)
- Hai-Ping Zhang
- 1] State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361102, PR China [2]
| | - Jian-Bo Pan
- 1] Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian, 361005, PR China [2]
| | - Chi Zhang
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361102, PR China
| | - Nan Ji
- State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361102, PR China
| | - Hao Wang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian, 361005, PR China
| | - Zhi-Liang Ji
- 1] State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361102, PR China [2] Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian, 361005, PR China
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15
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Lagunin AA, Goel RK, Gawande DY, Pahwa P, Gloriozova TA, Dmitriev AV, Ivanov SM, Rudik AV, Konova VI, Pogodin PV, Druzhilovsky DS, Poroikov VV. Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review. Nat Prod Rep 2014; 31:1585-611. [DOI: 10.1039/c4np00068d] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
An overview of databases andin silicotools for discovery of the hidden therapeutic potential of medicinal plants.
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Affiliation(s)
- Alexey A. Lagunin
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
| | - Rajesh K. Goel
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | - Dinesh Y. Gawande
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | - Priynka Pahwa
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | | | | | - Sergey M. Ivanov
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Anastassia V. Rudik
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Varvara I. Konova
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Pavel V. Pogodin
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
| | | | - Vladimir V. Poroikov
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
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16
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Reliably assessing prediction reliability for high dimensional QSAR data. Mol Divers 2012; 17:63-73. [DOI: 10.1007/s11030-012-9415-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2012] [Accepted: 12/03/2012] [Indexed: 10/27/2022]
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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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18
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Rao H, Zeng X, Wang Y, He H, Zhu F, Li Z, Chen Y. Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods. MOLECULAR SIMULATION 2012. [DOI: 10.1080/08927022.2011.616891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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19
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Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, Zhang L, Song Y, Liu X, Zhang J, Han B, Zhang P, Chen Y. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res 2012; 40:D1128-36. [PMID: 21948793 PMCID: PMC3245130 DOI: 10.1093/nar/gkr797] [Citation(s) in RCA: 353] [Impact Index Per Article: 29.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2011] [Revised: 09/08/2011] [Accepted: 09/09/2011] [Indexed: 01/10/2023] Open
Abstract
Knowledge and investigation of therapeutic targets (responsible for drug efficacy) and the targeted drugs facilitate target and drug discovery and validation. Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/ttd/ttd.asp) has been developed to provide comprehensive information about efficacy targets and the corresponding approved, clinical trial and investigative drugs. Since its last update, major improvements and updates have been made to TTD. In addition to the significant increase of data content (from 1894 targets and 5028 drugs to 2025 targets and 17,816 drugs), we added target validation information (drug potency against target, effect against disease models and effect of target knockout, knockdown or genetic variations) for 932 targets, and 841 quantitative structure activity relationship models for active compounds of 228 chemical types against 121 targets. Moreover, we added the data from our previous drug studies including 3681 multi-target agents against 108 target pairs, 116 drug combinations with their synergistic, additive, antagonistic, potentiative or reductive mechanisms, 1427 natural product-derived approved, clinical trial and pre-clinical drugs and cross-links to the clinical trial information page in the ClinicalTrials.gov database for 770 clinical trial drugs. These updates are useful for facilitating target discovery and validation, drug lead discovery and optimization, and the development of multi-target drugs and drug combinations.
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Affiliation(s)
- Feng Zhu
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Zhe Shi
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Chu Qin
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Lin Tao
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Xin Liu
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Feng Xu
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Li Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Yang Song
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Xianghui Liu
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Jingxian Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Bucong Han
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Peng Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, People's Republic of China, State Key Laboratory of Medicinal Chemistry & Biology, Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore 117543 and Computational Biology Program, National University of Singapore, Singapore 117543
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Demir-Kavuk O, Bentzien J, Muegge I, Knapp EW. DemQSAR: predicting human volume of distribution and clearance of drugs. J Comput Aided Mol Des 2011; 25:1121-33. [DOI: 10.1007/s10822-011-9496-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2011] [Accepted: 11/13/2011] [Indexed: 12/11/2022]
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21
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Tong J, Che T, Liu S, Li Y, Wang P, Xu X, Chen Y. SVEEVA Descriptor Application to Peptide QSAR. Arch Pharm (Weinheim) 2011; 344:719-25. [DOI: 10.1002/ardp.201100093] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2011] [Revised: 05/21/2011] [Accepted: 06/10/2011] [Indexed: 11/07/2022]
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22
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QSAR study on the interactions between antibiotic compounds and DNA by a hybrid genetic-based support vector machine. MONATSHEFTE FUR CHEMIE 2011. [DOI: 10.1007/s00706-011-0493-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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23
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Zhao ML, Yin JJ, Li ML, Xue Y, Guo Y. QSAR study for cytotoxicity of diterpenoid tanshinones. Interdiscip Sci 2011; 3:121-7. [DOI: 10.1007/s12539-011-0077-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2010] [Revised: 02/22/2011] [Accepted: 03/01/2011] [Indexed: 11/25/2022]
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24
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Punetha A, Shanmugam K, Sundar D. Insight into the Enzyme-Inhibitor Interactions of the First Experimentally Determined Human Aromatase. J Biomol Struct Dyn 2011; 28:759-71. [DOI: 10.1080/07391102.2011.10508604] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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25
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Yap CW. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J Comput Chem 2010; 32:1466-74. [PMID: 21425294 DOI: 10.1002/jcc.21707] [Citation(s) in RCA: 1560] [Impact Index Per Article: 111.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2010] [Revised: 08/22/2010] [Accepted: 10/12/2010] [Indexed: 11/08/2022]
Affiliation(s)
- Chun Wei Yap
- Department of Pharmacy, Pharmaceutical Data Exploration Laboratory, National University of Singapore, Singapore.
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26
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Maji P, Paul S. Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules. ACTA ACUST UNITED AC 2010. [DOI: 10.1109/tsmcc.2010.2047943] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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27
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A Study of Genetic Algorithm Evolution on the Lipophilicity of Polychlorinated Biphenyls. Chem Biodivers 2010; 7:1978-89. [DOI: 10.1002/cbdv.200900356] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Lv W, Xue Y. Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods. Eur J Med Chem 2010; 45:1167-72. [DOI: 10.1016/j.ejmech.2009.12.038] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2009] [Revised: 12/15/2009] [Accepted: 12/17/2009] [Indexed: 11/28/2022]
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Goodarzi M, Deshpande S, Murugesan V, Katti S, Prabhakar Y. Is Feature Selection Essential for ANN Modeling? ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200960074] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Zhu F, Han B, Kumar P, Liu X, Ma X, Wei X, Huang L, Guo Y, Han L, Zheng C, Chen Y. Update of TTD: Therapeutic Target Database. Nucleic Acids Res 2009; 38:D787-91. [PMID: 19933260 PMCID: PMC2808971 DOI: 10.1093/nar/gkp1014] [Citation(s) in RCA: 203] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets, hundreds of which are targets of approved and clinical trial drugs. Knowledge of these targets and corresponding drugs, particularly those in clinical uses and trials, is highly useful for facilitating drug discovery. Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. In order to accommodate increasing demand for comprehensive knowledge about the primary targets of the approved, clinical trial and experimental drugs, numerous improvements and updates have been made to TTD. These updates include information about 348 successful, 292 clinical trial and 1254 research targets, 1514 approved, 1212 clinical trial and 2302 experimental drugs linked to their primary targets (3382 small molecule and 649 antisense drugs with available structure and sequence), new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, standardized target ID, and significant increase of data (1894 targets, 560 diseases and 5028 drugs compared with the 433 targets, 125 diseases and 809 drugs in the original release described in previous paper). This database can be accessed at http://bidd.nus.edu.sg/group/cjttd/TTD.asp.
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Affiliation(s)
- Feng Zhu
- Department of Pharmacy and Computation and Systems Biology, Center for Computational Science and Engineering, Singapore-MIT Alliance, National University of Singapore, Singapore
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31
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Yu Y, Su R, Wang L, Qi W, He Z. Comparative QSAR modeling of antitumor activity of ARC-111 analogues using stepwise MLR, PLS, and ANN techniques. Med Chem Res 2009. [DOI: 10.1007/s00044-009-9266-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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32
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Vasina EN, Paszek E, Nicolau DV, Nicolau DV. The BAD project: data mining, database and prediction of protein adsorption on surfaces. LAB ON A CHIP 2009; 9:891-900. [PMID: 19294299 DOI: 10.1039/b813475h] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.
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Affiliation(s)
- Elena N Vasina
- Department of Electrical Engineering & Electronics, The University of Liverpool, Liverpool, L69 3GJ, UK
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Wang M, Yang XG, Xue Y. Identifying hERG Potassium Channel Inhibitors by Machine Learning Methods. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200810015] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction. J Comput Aided Mol Des 2008; 22:843-55. [DOI: 10.1007/s10822-008-9225-4] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2007] [Accepted: 06/08/2008] [Indexed: 02/07/2023]
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