Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery

By Feng Zhu, Zhe Shi, Chu Qin, Lin Tao, Xin Liu, Feng Xu, Li Zhang, Yang Song, Xianghui Liu, Jing-Xian Zhang and 3 more. Nucleic Acids Res., 2011

480 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology

View Paper: https://www.semanticscholar.org/paper/854912786e796e5e41ea4ed8f05520666932f173

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Citations in the corpus

STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data (2015, background): “A large part of the known interactions comes from manually curated datasets such as DrugBank (19), GPCR-ligand database (GLIDA) (20), Matador (21), the Therapeutic Targets Database (TTD) (22) and the Comparative Toxicogenomics Database (CTD) (23), and several pathway databases including the Kyoto En”
Network-based approach to prediction and population-based validation of in silico drug repurposing (2018, unknown): “02) (59), and the PharmGKB database (December 30, 2015) (60).”
BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (2016, background): “Known targets of drugs/ingredients were from DrugBank (downloaded on July 26, 2015), KEGG (version: July 31, 2014) and TTD (Therapeutic Target Database) (version: 4.3.02) 51 .”
Genetics of rheumatoid arthritis contributes to biology and drug discovery (2013, unknown): “To test this ‘therapeutic hypothesis’ 1 , we obtained 871 drug target genes corresponding to approved, in clinical trials or experimental drugs for human diseases 17,18 (Supplementary Table 6).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “The mistaken identification of unexpected edges as spurious edges (Lü & Zhou, 2011), and the better predictability of edges in dense cores than those in network periphery (Zhu et al., 2012a) are both related to the inherent unpredictability caused by network dynamics.”
The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands (2013, background): “In comparison, the two literature references for atorvastatin in TTD are not the same as from the other three sources.”
DGIdb 2.0: mining clinically relevant drug–gene interactions (2015, methodology): “In contrast, the ‘non-curated’ sources, such as GO (26), PharmGKB (9), DrugBank (10) and TTD (7), are generally more inclusive of putative interactions and more comprehensive.”
The ConsensusPathDB interaction database: 2013 update (2012, background): “The newly integrated resources are BIND (protein–protein interactions) (10), DrugBank (drug–target interactions) (11), InnateDB (protein–protein, biochemical and gene regulatory interactions) (12), MatrixDB (protein–protein interactions) (13), PDZBase (protein–protein interactions) (14), PhosphoPOIN”
STITCH 4: integration of protein–chemical interactions with user data (2013, background): “The second channel is made up of manually curated drug–target databases: DrugBank (12), GLIDA (13), Matador (14), TTD (15) and CTD (16); and pathway databases: KEGG (17), NCI/Nature Pathway Interaction Database (18), Reactome (19) and BioCyc (20) .”
SuperPred: update on drug classification and target prediction (2014, methodology): “For the evaluation of the target prediction, the dataset was restricted to ‘successful targets’ from the Therapeutic Target Database (TTD) (25) narrowing the set to 221 targets, 95 000 compounds and 174 000 compound–target interactions.”