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 assays. Based on
these annotated pharmacological data, VDDB allows users to browse, search and
download reliable information about these collects for various viruses of
interest. In particular, VDDB also integrates 57 cell infection- and 117
target-based associated high-accuracy machine learning models to support
various antivirals identification-related tasks, such as compound activity
prediction, virtual screening, drug repositioning and target fishing. VDDB is
freely accessible at this http URL.

本研究提出了一个开放查询的抗病毒药物资源库和机器学习平台，拥有手工筛选的关于试验验证可能抗病毒药物分子的数据集，并支持药物活性预测、虚拟筛选和其他相关任务。