论文标题
使用深神经网络的SARS-COV-2抑制剂基于大规模配体的虚拟筛选
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
论文作者
论文摘要
由于当前严重的急性呼吸道综合征冠状病毒2（SARS-COV-2）大流行，因此迫切需要新的疗法和药物。我们对潜在的COV-2抑制剂进行了大规模的虚拟筛选。为此，我们利用了“ Chemai”，这是一个深层神经网络，对来自三个公共药物发现数据库的360万个分子的数据点进行了超过2000万个数据点的培训。使用Chemai，我们从锌数据库中筛选并排名10亿个分子，以对COV-2进行有利的影响。然后，我们将结果减少到了30,000个顶级化合物，可以通过锌数据库易于访问和购买。此外，我们使用Chemai筛选了药品库以进行药物重新利用，这将是一种快速的治疗方法。我们在https://github.com/ml-jku/sars-cov-inhipitors-chemai上提供这些排名最高的锌和药品库的化合物作为图书馆，以进一步筛选。
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.
