论文标题
SARS-COV-2蛋白酶抑制剂的进化多目标设计
Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates
论文作者
论文摘要
基于人工智能的计算药物设计是一个新兴研究领域。在撰写本文时，世界遭受了冠状病毒SARS-COV-2的爆发。阻止病毒复制的一种有希望的方法是通过蛋白酶抑制。我们提出了一种进化的多目标算法（EMOA），以设计SARS-COV-2的主要蛋白酶的潜在蛋白酶抑制剂。根据自拍照表示，EMOA使用对接工具QuickVina 2最大化候选配体与蛋白质的结合，同时考虑到诸如毒品类似或实现滤光片约束的进一步目标。实验部分分析了进化过程，并讨论了抑制剂候选物。
Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeliness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.
