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arXiv 2004.00979q-bio.BMcs.LGq-bio.QMstat.ML
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基于配体的深度神经网络大规模虚拟筛选SARS-CoV-2抑制剂

Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks

ELLIS Unit at the LIT AI Lab, Johannes Kepler University Linz(林茨约翰内斯·开普勒大学 LIT AI 实验室 ELLIS 单元);Institute for Machine Learning, Johannes Kepler University Linz(林茨约翰内斯·开普勒大学机器学习研究所)

Markus Hofmarcher, Andreas Mayr, Elisabeth Rumetshofer, Peter Ruch, Philipp Renz, Johannes Schimunek, Philipp Seidl, Andreu Vall, Michael Widrich, Sepp Hochreiter, Günter Klambauer

摘要:

由于当前严重急性呼吸综合征冠状病毒2(SARS-CoV-2)大流行,迫切需要新型疗法和药物。我们进行了大规模的虚拟筛选,寻找潜在的CoV-2抑制剂小分子。为此,我们利用了“ChemAI”,这是一个深度神经网络,在来自三个公共药物发现数据库的360万个分子中的超过2.2亿个数据点上进行了训练。通过ChemAI,我们从ZINC数据库中筛选并排名了10亿个分子,以获得对CoV-2的有利影响。然后,我们将结果简化为30,000种排名最高的化合物,这些化合物可以通过ZINC数据库轻松访问和购买。此外,我们使用ChemAI筛选DrugBank以允许药物再利用,这将是一种快速的治疗方法。我们在https://github.com/ml-jku/sars-cov-inhibitors-chemai上提供这些ZINC和DrugBank的顶级化合物作为进一步筛选生物测定的库。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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