Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents

Author:

Zhou Ying1,Zhang Yintao2,Lian Xichen2,Li Fengcheng2,Wang Chaoxin3,Zhu Feng24ORCID,Qiu Yunqing1,Chen Yuzong56ORCID

Affiliation:

1. State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China

2. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China

3. Department of Computer Science, Kansas State University, Manhattan 66506, USA

4. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China

5. State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China

6. Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China

Abstract

Abstract Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

Funder

Ningbo University

Ningbo Top Talent

Zhejiang Provincial Science and Technology Department

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Fundamental Research Fund for the Central Universities

‘Double Top-Class’ University Project

Key R&D Program of Zhejiang Province

Alibaba-Zhejiang University

Alibaba Cloud

Information Technology Center of Zhejiang University

Publisher

Oxford University Press (OUP)

Subject

Genetics

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