VDDB: a comprehensive resource and machine learning platform for antiviral drug discovery
Shunming Tao, Yihao Chen, Jingxing Wu, Duancheng Zhao, Hanxuan Cai,, Ling Wang

TL;DR
VDDB is an open-access platform that consolidates verified antiviral drug data and integrates machine learning models to facilitate drug discovery, targeting viruses like SARS-CoV-2 with comprehensive pharmacological and assay data.
Contribution
This work introduces the first comprehensive resource combining curated antiviral drug data with machine learning models for drug discovery tasks.
Findings
Contains data on 848 vaccines, 199 antibodies, 710,000 small molecules
Stores 3 million pharmacological records and assay data
Integrates 174 machine learning models for antiviral research
Abstract
Virus infection is one of the major diseases that seriously threaten human health. To meet the growing demand for mining and sharing data resources related to antiviral drugs and to accelerate the design and discovery of new antiviral drugs, we presented an open-access antiviral drug resource and machine learning platform (VDDB), which, to the best of our knowledge, is the first comprehensive dedicated resource for experimentally verified potential drugs/molecules based on manually curated data. Currently, VDDB highlights 848 clinical vaccines, 199 clinical antibodies, as well as over 710,000 small molecules targeting 39 medically important viruses including SARS-CoV-2. Furthermore, VDDB stores approximately 3 million records of pharmacological data for these collected potential antiviral drugs/molecules, involving 314 cell infection-based phenotypic and 234 target-based genotypic…
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Taxonomy
TopicsComputational Drug Discovery Methods · vaccines and immunoinformatics approaches · Influenza Virus Research Studies
