浙江大学 | 药学院 | English Version
     
     
IDRB: 数据库
据库构建

本课题组已成功建立多个生物信息学和药物信息学数据库,具体如下:

  TTD: Therapeutic Target Database
    Database URL: https://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 Research (当年影响因子: 11.501, 生物一区 TOP 期刊). 48(D1): 1031-1041 (2020). PMID: 31691823.
  2. 中国百篇最具影响国际学术论文: ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.13% in 2022.
    • The Percentile in Subject Area shown in InCites™ was 0.15% in 2021.
    科技媒体及新闻报道:

  3. 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 Research (当年影响因子: 11.561, 生物一区 TOP 期刊). 46(D1): 1121-1127 (2018). PMID: 29140520.
  4. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.21% in 2022.
    • 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.
    领域内专家评论:
    • Introduced by OMICTOOLS as "useful for facilitating patient focused research, discovery and clinical investigations of the targeted therapeutics".

  5. Y. Zhou, Y. T. Zhang, X. C. Lian, F. C. Li, C. X. Wang, F. Zhu*, Y. Q. Qiu*, Y. Z. Chen*. Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Research (当年影响因子: 16.971, 生物一区 TOP 期刊). 50(D1): 1398-1407 (2022). PMID: 34718717.
  6. 科技媒体及新闻报道:

  7. 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 Research (当年影响因子: 9.202, 生物一区 TOP 期刊). 44(D1): 1069-1074 (2016). PMID: 26578601.
  8. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.99% in 2022.
    • The Percentile in Subject Area shown in InCites™ was 0.83% 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.

  9. 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 Research (当年影响因子: 8.026, 生物一区 TOP 期刊). 40(D1): 1128-1136 (2012). PMID: 21948793.
  10. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.77% in 2022.
    • 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.

    领域内专家评论:

    • "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".

  11. 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 Research (当年影响因子: 7.479, 生物一区 TOP 期刊). 38(D1): 787-791 (2010). PMID: 19933260.
  12. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 2.95% in 2017.
  VARIDT: VARIability of Drug Transporter Database
    Database URL: https://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://idrblab.org/varidt/.

    Our Publication(s) Describing This Database:

  1. T. T. Fu, F. C. Li, Y. Zhang, J. Y. Yin, W. Q. Qiu, X. D. Li, X. G. Liu, W. W. Xin, C. Z. Wang, L. S. Yu, J. Q. Gao, Q. C. Zheng*, S. Zeng*, F. Zhu*. VARIDT 2.0: structural variability of drug transporter. Nucleic Acids Research (当年影响因子: 16.971, 生物一区 TOP 期刊). 50(D1): 1417-1431 (2022). PMID: 34747471.
  2. 科技媒体及新闻报道:

  3. 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 Research (当年影响因子: 11.501, 生物一区 TOP 期刊). 48(D1): 1042-1050 (2020). PMID: 31495872.
  4. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 0.96% in 2022.
    • The Percentile in Subject Area shown in InCites™ was 0.33% in 2021.
    科技媒体及新闻报道:

  INTEDE: Interactome of Drug-metabolizing Enzymes
    Database URL: https://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 Research (当年影响因子: 16.971, 生物一区 TOP 期刊). 49(D1): 1233-1243 (2021). PMID: 33045737.
  2. ESI高被引或扩展高被引论文:
    • The Percentile in Subject Area shown in InCites™ was 1.12% in 2022.
    科技媒体及新闻报道:

  DrugMAP: Molecular Atlas and Pharma-Information of Drugs
    Database URL: https://idrblab.org/drugmap/

    The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

    Our Publication(s) Describing This Database:

  1. F. C. Li, J. Y. Yin, M. K. Lu, M. J. Mou, Z. R. Li, Z. Y. Zeng, Y. Tan, S. S. Wang, X. Y. Chu, H. B. Dai, T. J. Hou, S. Zeng*, Y. Z. Chen*, F. Zhu*. DrugMAP: molecular atlas and pharma-information of drugs. Nucleic Acids Research (impact factor of the publication year: 19.160, 生物一区 TOP 期刊). doi: 10.1093/nar/gkac813 (2023). PMID: 36243961.
  2. Media Coverage & News Report:

  DRESIS: A Comprehensive Database for Drug Resistance Information
    Database URL: https://idrblab.org/dresis/

    Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named ‘DRESIS’ was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

    Our Publication(s) Describing This Database:

  1. X. N. Sun, Y. T. Zhang, H. Y. Li, Y. Zhou, S. Y. Shi, Z. Chen, X. He, H. Y. Zhang, F. C. Li, J. Y. Yin, M. J. Mou, Y. Z. Wang, Y. Q. Qiu, F. Zhu*. DRESIS: a comprehensive database for drug resistance information. Nucleic Acids Research (当年影响因子: 19.160, 生物一区 TOP 期刊). doi: 10.1093/nar/gkac812 (2023). PMID: 36243960.
  GIMICA: Host Genetic and Immune Factors Shaping Human Microbiota
    Database URL: https://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 Research (当年影响因子: 16.971, 生物一区 TOP 期刊). 49(D1): 715-722 (2021). PMID: 33045729.
  2. 科技媒体及新闻报道:

  CovInter: Interaction Data between Coronavirus RNAs and Host Proteins
    Database URL: https://idrblab.org/covinter/

    Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus–host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the ‘molecular arms race’ between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.

    Our Publication(s) Describing This Database:

  1. K. Amahong, W. Zhang, Y. Zhou, S. Zhang, J. Y. Yin, F. C. Li, H. Q. Xu, T. C, Yan, Z. X. Yue, Y. H. Liu, T. J. Hou, Y. Q. Qi, L. Tao*, L. Y. Han*, F. Zhu*. CovInter: interaction data between coronavirus RNAs and host proteins. Nucleic Acids Research (当年影响因子: 19.160, 生物一区 TOP 期刊). doi: 10.1093/nar/gkac834 (2023). PMID: 36200814.
  2. 科技媒体及新闻报道:

  SYNBIP: SYNthetic BInding Proteins for Research, Diagnosis and Therapy
    Database URL: https://idrblab.org/synbip/

    The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP; and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.

    Our Publication(s) Describing This Database:

  1. X. N. Wang, F. C. Li, W. Q. Qiu, B. B. Xu, Y. L. Li, X. C. Lian, H. Y. Yu, Z. Zhang, J. X. Wang, Z. R. Li, W. W. Xue*, F. Zhu*. SYNBIP: synthetic binding proteins for research, diagnosis and therapy. Nucleic Acids Research (当年影响因子: 16.971, 生物一区 TOP 期刊). 50(D1): 560-570 (2022). PMID: 34664670.
  2. 科技媒体及新闻报道:

  NPCDR: Natural Product-based Drug Combination and Its Disease-specific Molecular Regulation
    Database URL: https://idrblab.org/npcdr/

    Natural product (NP) has a long history in promoting modern drug discovery, which has derived or inspired a large number of currently prescribed drugs. Recently, the NPs have emerged as the ideal candidates to combine with other therapeutic strategies to deal with the persistent challenge of conventional therapy, and the molecular regulation mechanism underlying these combinations is crucial for the related communities. Thus, it is urgently demanded to comprehensively provide the disease-specific molecular regulation data for various NP-based drug combinations. However, no database has been developed yet to describe such valuable information. In this study, a newly developed database entitled ‘Natural Product-based Drug Combination and Its Disease-specific Molecular Regulation (NPCDR)’ was thus introduced. This database was unique in (a) providing the comprehensive information of NP-based drug combinations & describing their clinically or experimentally validated therapeutic effect, (b) giving the disease-specific molecular regulation data for a number of NP-based drug combinations, (c) fully referencing all NPs, drugs, regulated molecules/pathways by cross-linking them to the available databases describing their biological or pharmaceutical characteristics. Therefore, NPCDR is expected to have great implications for the future practice of network pharmacology, medical biochemistry, drug design, and medicinal chemistry. This database is now freely accessible without any login requirement at both official (https://idrblab.org/npcdr/) and mirror (http://npcdr.idrblab.net/) sites.

    Our Publication(s) Describing This Database:

  1. X. N. Sun, Y. T. Zhang, Y. Zhou, X. C. Lian, L. L. Yan, T. Pan, T. Jin, H. Xie, Z. M. Liang, W. Q. Qiu, J. X. Wang, Z. R. Li, F. Zhu*, X. B. Sui*. NPCDR: natural product-based drug combination and its disease-specific molecular regulation. Nucleic Acids Research (当年影响因子: 16.971, 生物一区 TOP 期刊). 50(D1): 1324-1333 (2022). PMID: 34664659.
  REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research
    Database URL: https://idrblab.org/regliv/

    Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

    Our Publication(s) Describing This Database:

  1. S. Zhang, X. N. Sun, M. J. Mou, K. Amahong, H. C. Sun, W. Zhang, S. Y. Shi, Z. R. Li, J. Q. Gao, F. Zhu*. REGLIV: molecular regulation data of diverse living systems facilitating current multiomics research.  Computers in Biology and Medicine (当年影响因子: 6.698, 工程技术二区期刊). 148: 105825 (2022). PMID: 35872412
IDRB: Innovative Drug Research and Bioinformatics Group


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