基于结构的生物活性优化的深度学习模型及其在SARS-CoV-2主要蛋白酶抑制剂生物活性优化中的应用
Zhenyu Yang1, Kai Wang2, Guo Zhang2
1West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
European journal of medicinal chemistry
|April 16, 2025
概括
一个深度学习模型Pocket-StrMod通过优化分子生物活性来加速药物发现. 这种基于结构的方法显著改善了SARS-CoV-2抑制剂.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- 生物活性优化在早期药物发现中至关重要,但传统上耗时且昂贵.
- 代替代物优化是一种常见但低效的方法.
- 开发更快,更具成本效益的优化策略至关重要.
研究的目的:
- 介绍Pocket-StrMod,一种用于基于结构的生物活性优化的新型深度学习模型.
- 为了证明模型能够优化蛋白质结合口袋中的分子.
- 应用该模型来增强SARS-CoV-2主要蛋白酶抑制剂的生物活性.
主要方法:
- 使用了一种基于流程的自回归深度学习架构.
- 集成的化学专业知识用于同步替代剂优化.
- 在蛋白质口袋内的分子支架内生成的原子和共价键.
主要成果:
- 优化Hit1,一个SARS-CoV-2 Mpro抑制剂,初始IC50为34.56μM.
- 发现一种C5化合物,其生物活性提高了1028倍 (IC50:33.6nM).
- C5在体外表现出对SARS-CoV-2的有希望的抗病毒活性.
结论:
- 通过Pocket-StrMod的深度学习,可以快速,经济高效地优化生物活性.
- 该模型显示了加速早期药物开发的巨大潜力.
- 基于结构的深度学习是设计强效治疗剂的强大工具.
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