Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics

By Yunxia Wang, Song-zhao Zhang, Fengcheng Li, Ying Zhou, Ying Zhang, Zhengwen Wang, Runyuan Zhang, Jiang Zhu, Yuxiang Ren, Ying Tan and 5 more. Nucleic Acids Res., 2019

769 citations recorded by the source corpus.

Abstract Knowledge of therapeutic targets and early drug candidates is useful for improved drug discovery. In particular, information about target regulators and the patented therapeutic agents facilitates research regarding druggability, systems pharmacology, new trends, molecular landscapes, and the development of drug discovery tools. To complement other databases, we constructed the Therapeutic Target Database (TTD) with expanded information about (i) target-regulating microRNAs and transcription factors, (ii) target-interacting proteins, and (iii) patented agents and their targets (structures and experimental activity values if available), which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes. We also updated the TTD with the recently released International Classification of Diseases ICD-11 codes and additional sets of successful, clinical trial, and literature-reported targets that emerged since the last update. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. In case of possible web connectivity issues, two mirror sites of TTD are also constructed (http://db.idrblab.org/ttd/ and http://db.idrblab.net/ttd/).

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e2e9efd6f1bee9ffecbbf8cd91eac1a56673bbeb

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Citations in the corpus

HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine (2020, methodology): “We further standardized gene names for the newly curated TCM targets according to the NCBI nomenclature and GeneCards database (37), and then cross-referenced them with the TTD database (17), a widely used therapeutic target database for modern compounds.”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “…example, BindingDB (Liu et al. 2007) curates binding affinity data, ChEMBL (Mendez et al. 2019) curates bioassay data, THPdb (Usmani et al. 2017) and TTD (Wang et al. 2020) record information on therapeutic targets, and BioSNAP Datasets (Zitnik, Sosic & Leskovec 2018) contains biological networks.”
Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts (2020, background): “We have also updated multiple sources including large, well-curated sources such as DrugBank (21), Guide to Pharmacology (22), Gene Ontology (23,24), OncoKB (25), PharmGKB (26), and the Therapeutic Target Database (27) (Figure 2, Supplementary Table S1).”