Zhejiang U | College of Pharmaceutical Sciences | 中文版
     
     
IDRB: Databases
DATABASE CONSTRUCTION

Our experiences on database construction have led to several pharmacoinformatics databases as follows:
  TTD: Therapeutic Target Database
    Database URL: http://db.idrblab.org/ttd/

    Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Therefore, we developed the Therapeutic Target Database (TTD) to provide information about known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and the corresponding drugs directed at each of these targets. TTD was known to be one of the most popular pharmaceutical databases around the world, and included the links to relevant databases containing information about target function, sequence, 3D structure, ligand binding properties, enzyme nomenclature and drug structure, therapeutic class, and clinical development status.

    Our Publication(s) Describing This Database:

  1. Y. X. Wang, S. Zhang, F. C. Li, Y. Zhou, Y. Zhang, Z. W. Wang, R. Y. Zhang, J. Zhu, Y. X. Ren, Y. Tan, C. Qin, Y. H. Li, X. X. Li, Y. Z. Chen*, F. Zhu*. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res (impact factor of the publication year: 11.501, 生物一区 TOP 期刊). 48(D1): 1031-1041 (2020).  
    2. ESI Highly Cited Paper:
    • The Percentile in Subject Area shown in InCites™ was 0.15% in 2021.
    Media Coverage & News Report:

  2. Y. H. Li, C. Y. Yu, X. X. Li, P. Zhang, J. Tang, Q. X. Yang, T. T. Fu, X. Y. Zhang, X. J. Cui, G. Tu, Y. Zhang, S. Li, F. Y. Yang, Q. Sun, C. Qin, X. Zeng, Z. Chen, Y. Z. Chen*, F. Zhu*. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res (impact factor of the publication year: 11.561, 生物一区 TOP 期刊). 46(D1): 1121-1127 (2018).  
    3. ESI Highly Cited Paper:
    • The Percentile in Subject Area shown in InCites™ was 0.15% in 2021.
    • The Percentile in Subject Area shown in InCites™ was 0.14% in 2020.
    • The Percentile in Subject Area shown in InCites™ was 0.16% in 2019.
    Highlights by Experts in Subject Area:
    • Introduced by OMICTOOLS as "useful for facilitating patient focused research, discovery and clinical investigations of the targeted therapeutics".

  3. H. Yang, C. Qin, Y. H. Li, L. Tao, J. Zhou, C. Y. Yu, F. Xu, Z. Chen, F. Zhu*, Y. Z. Chen*. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res (impact factor of the publication year: 9.202, 生物一区 TOP 期刊). 44(D1): 1069-1074 (2016).  
    4. ESI Highly Cited Paper:
    • The Percentile in Subject Area shown in InCites™ was 0.69% in 2021.
    • The Percentile in Subject Area shown in InCites™ was 0.78% in 2020.
    • The Percentile in Subject Area shown in InCites™ was 0.66% in 2019.
    • The Percentile in Subject Area shown in InCites™ was 0.71% in 2018.
    • The Percentile in Subject Area shown in InCites™ was 0.87% in 2017.

  4. F. Zhu, Z. Shi, C. Qin, L. Tao, X. Liu, F. Xu, L. Zhang, Y. Song, X. H. Liu, J. X. Zhang, B. C. Han, P. Zhang, Y. Z. Chen*. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res (impact factor of the publication year: 8.026, 生物一区 TOP 期刊). 40(D1): 1128-1136 (2012).  
    5. ESI Highly Cited Paper:
    • The Percentile in Subject Area shown in InCites™ was 0.69% in 2021.
    • The Percentile in Subject Area shown in InCites™ was 0.66% in 2020.
    • The Percentile in Subject Area shown in InCites™ was 0.60% in 2019.
    • The Percentile in Subject Area shown in InCites™ was 0.62% in 2018.
    • The Percentile in Subject Area shown in InCites™ was 0.31% in 2017.

    Highlights by Experts in Subject Area:

    • "FACULTYof1000" as "the top 2% of published articles in biology and medicine" and "a most useful resource for scientists and companies working on drug discovery and validation, drug lead discovery and optimization, and the development of multi-target drugs and drug combinations".
    • Prof. Chris Southan in his blog as "Therapeutic Target Database in PubChem".

  5. F. Zhu, B. C. Han, P. Kumar, X. H. Liu, X. H. Ma, X. N. Wei, L. Huang, Y. F. Guo, L. Y. Han, C. J. Zheng, Y. Z. Chen*. Update of TTD: therapeutic target database. Nucleic Acids Res (impact factor of the publication year: 7.479, 生物一区 TOP 期刊). 38(D1): 787-791 (2010).  
    6. ESI Highly Cited Paper:
    • The Percentile in Subject Area shown in InCites™ was 2.95% in 2017.
  VARIDT: VARIability of Drug Transporter Database
    Database URL: https://db.idrblab.org/varidt/

    The absorption, distribution and excretion of drugs are largely determined by their transporters (DTs), the variability of which has thus attracted considerable attention. There are three aspects of variability: epigenetic regulation and genetic polymorphism, species/tissue/disease-specific DT abundances, and exogenous factors modulating DT activity. The variability data of each aspect are essential for clinical study, and a collective consideration among multiple aspects becomes essential in precision medicine. However, no database is constructed to provide the comprehensive data of all aspects of DT variability. Herein, the Variability of Drug Transporter Database (VARIDT) was introduced to provide such data. First, 177 and 146 DTs were confirmed, for the first time, by the transporting drugs approved and in clinical/preclinical, respectively. Second, for the confirmed DTs, VARIDT comprehensively collected all aspects of their variability (23,947 DNA methylations, 7,317 noncoding RNA/histone regulations, 1,278 genetic polymorphisms, differential abundance profiles of 257 DTs in 21,781 patients/healthy individuals, expression of 245 DTs in 67 tissues of human/model organism, 1,225 exogenous factors altering the activity of 148 DTs), which allowed mutual connection between any aspects. Due to huge amount of accumulated data, VARIDT made it possible to generalize characteristics to reveal disease etiology and optimize clinical treatment, and is freely accessible at: https://db.idrblab.org/varidt/.

    Our Publication(s) Describing This Database:

  1. J. Y. Yin, W. Sun, F. C. Li, J. J. Hong, X. X. Li, Y. Zhou, Y. J. Lu, M. Z. Liu, X. Zhang, N. Chen, X. P. Jin, J. Xue, S. Zeng*, L. S. Yu*, F. Zhu*. VARIDT 1.0: variability of drug transporter database. Nucleic Acids Res (impact factor of the publication year: 11.501, 生物一区 TOP 期刊). 48(D1): 1042-1050 (2020).  
    2. ESI Highly Cited Paper:
    • The Percentile in Subject Area shown in InCites™ was 0.33% in 2021.
    Media Coverage & News Report:

  INTEDE: Interactome of Drug-metabolizing Enzymes
    Database URL: https://db.idrblab.org/intede/

    Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC), and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1,047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3,359 MICBIOs between 225 microbial species and 185 DMEs; 47,778 XEOTICs between 4,150 xenobiotics and 501 DMEs; 7,849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.

    Our Publication(s) Describing This Database:

  1. J. Y. Yin, F. C. Li, Y. Zhou, M. J. Mou, Y. J. Lu, K. L. Chen, J. Xue, Y. C. Luo, J. B. Fu, X. He, J. Q. Gao, S. Zeng*, L. S. Yu*, F. Zhu*. INTEDE: interactome of drug-metabolizing enzymes. Nucleic Acids Res (impact factor of the publication year: 11.501, 生物一区 TOP 期刊). 49(D1): 1233-1243 (2021).  
    2. Media Coverage & News Report:

  GIMICA: Host Genetic and Immune Factors Shaping Human Microbiota
    Database URL: https://db.idrblab.org/gimica/

    Besides the environmental factors having tremendous impacts on the composition of microbial community, the host factors have recently gained extensive attentions on their roles in shaping human microbiota. There are two major types of host factors: host genetic factors (HGFs) and host immune factors (HIFs). These factors of each type are essential for defining the chemical and physical landscapes inhabited by microbiota, and the collective consideration of both types have great implication to serve comprehensive health management. However, no database was available to provide the comprehensive factors of both types. Herein, a database entitled ‘Host Genetic and Immune Factors Shaping Human Microbiota (GIMICA)’ was constructed. Based on the 4,257 microbes confirmed to inhabit nine sites of human body, 2,851 HGFs (1,368 single nucleotide polymorphisms (SNPs), 186 copy number variations (CNVs), and 1,297 non-coding ribonucleic acids (RNAs)) modulating the expression of 370 microbes were collected, and 549 HIFs (126 lymphocytes and phagocytes, 387 immune proteins, and 36 immune pathways) regulating the abundance of 455 microbes were also provided. All in all, GIMICA enabled the collective consideration not only between different types of host factor but also between the host and environmental ones, which is freely accessible without login requirement at: https://idrblab.org/gimica/.

    Our Publication(s) Describing This Database:

  1. J. Tang, X. L. Wu, M. J. Mou, C. Wang, L. D. Wang, F. C. Li, M. Y. Guo, J. Y. Yin, W. Q. Xie, X. N. Wang, Y. X. Wang, Y. B. Ding*, W. W. Xue*, F. Zhu*. GIMICA: host genetic and immune factors shaping human microbiota. Nucleic Acids Res (impact factor of the publication year: 11.501, 生物一区 TOP 期刊). 49(D1): 715-722 (2021).  
    2. Media Coverage & News Report:

IDRB: Innovative Drug Research and Bioinformatics Group


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College of Pharmaceutical Sciences, Zhejiang University
Hangzhou, P.R. China, 310058.
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