一种可解释的深度学习和分子对接框架,用于将现有药物作为SARS-CoV-2主蛋白酶的抑制剂
Juan Huang1, Jialong Gao1, Qu Chen1
1School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
一个新的混合框架结合了深度学习和分子对接,以加速COVID-19的药物发现. 埃纳西代尼布具有作为SARS-CoV-2主要蛋白酶抑制剂的潜力,需要进一步调查.
科学领域:
- 计算化学
- 药物发现
- 生物信息学
背景情况:
- 尽管疫苗接种广泛,但COVID-19仍然是一个全球性健康挑战.
- 有效的药物查和重新定位对于开发新疗法至关重要.
- 现有识别潜在药物候选者的方法可能耗时.
研究的目的:
- 开发和验证结合深度学习和分子对接的新型混合框架.
- 为加快对COVID-19潜在治疗剂的识别.
- 确定SARS-CoV-2主要蛋白酶 (MPro) 的抑制剂.
主要方法:
- 使用深度学习模型对候选化合物进行初步快速选.
- 分子对接工具 (AutoDock Vina,LeDock) 评估了结合亲和力.
- 预测药物与蛋白质的结合部位与已知的活性残留物重叠.
主要成果:
- 该框架使用四种经过实验确认的COVID-19药物标对进行了验证.
- 在29种候选药物中,Enasidenib被确定为一个有前途的MPro抑制剂.
- 埃纳西代尼布符合所有三个选择标准,包括预测的相互作用评分,结合亲和力和结合部位重叠.
结论:
- 混合深度学习和分子对接框架为虚拟查和药物重新使用提供了有效的策略.
- 埃纳西代尼布具有作为MPro抑制剂的潜力,但需要进一步的实验和临床验证.
- 这种可解释的策略可以适用于其他药物发现目标和计算工具.
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