DrugMAP 2.0: molecular atlas and pharma-information of all drugs
The escalating costs and high failure rates have decelerated the pace of drug development, which amplifies the research interests in developing combinatorial/repurposed drugs and understanding off-target adverse drug reaction (ADR). In other words, it is demanded to delineate the molecular atlas and pharma-information for the combinatorial/repurposed drugs and off-target interactions. However, such invaluable data were inadequately covered by existing databases. In this study, a major update was thus conducted to the DrugMAP, which accumulated (a) 20831 combinatorial drugs and their interacting atlas involving 1583 pharmacologically important molecules; (b) 842 repurposed drugs and their interacting atlas with 795 molecules; (c) 3260 off-targets relevant to the ADRs of 2731 drugs and (d) various types of pharmaceutical information, including diverse ADMET properties, versatile diseases, and various ADRs/off-targets. With the growing demands for discovering combinatorial/repurposed therapies and the rapidly emerging interest in AI-based drug discovery, DrugMAP was highly expected to act as an indispensable supplement to existing databases facilitating drug discovery, which was accessible at: https://idrblab.org/drugmap/.
Authors
- Fengcheng Li (ORCID: https://orcid.org/0000-0002-1846-639X)
- Minjie Mou (ORCID: https://orcid.org/0000-0001-7619-2975)
- Yang Zhang (ORCID: https://orcid.org/0000-0002-4201-3702)
- Jiayi Yin (ORCID: https://orcid.org/0000-0001-9115-4571)
- LI Xiao-yi (ORCID: https://orcid.org/0000-0003-0449-6253)
- Feng Zhu (ORCID: https://orcid.org/0000-0001-8069-0053)
- Weize Xu (ORCID: https://orcid.org/0000-0002-3903-0683)
Institutions
- Hebei Medical University (CN)
- National Clinical Research (US)
- Artificial Intelligence in Medicine (Canada) (CA)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Nucleic Acids Research
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1093/nar/gkae791
- Citations
- 15
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 5.78
Funders
- National Natural Science Foundation of China
- Zhejiang University
- National Key Research and Development Program of China
- Natural Science Foundation of Zhejiang Province