机器学习在计算机辅助药物发现中的应用
Sm Bargeen Alam Turzo1, Eric R Hantz1, Steffen Lindert1
1Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, USA.
QRB discovery
|August 2, 2023
概括
深度学习 (DL) 通过分析用于预测模型的实验数据来推进基于结构的药物设计 (SBDD). 这次审查强调了DL的重点.
科学领域:
- 计算化学和化学信息学
- 人工智能在药物发现中的作用
背景情况:
- 机器学习正在改变基于结构的药物设计 (SBDD).
- 深度学习 (DL) 是ML的一个子集,擅长识别实验数据中的复杂模式.
- 由于其复杂的分析能力,DL在SBDD中越来越多地被采用.
研究的目的:
- 审查基于结构的药物设计 (SBDD) 中深度学习 (DL) 应用的最新趋势.
- 专注于关键的DL应用,包括新药设计,结合部位预测和结合亲和预测.
主要方法:
- 对SBDD的DL近期进展的文献综述.
- 对用于特定SBDD任务的DL方法的分析.
- 综合与新设计,结合点和结合亲和力预测相关的发现.
主要成果:
- DL模型在提高SBDD流程方面显示出巨大的潜力.
- 最近的DL趋势显示出新药设计的前景,提高了效率和新性.
- 预测结合点和亲和关系的DL应用程序变得越来越准确和可靠.
结论:
- 深度学习是一种强大的工具,可以彻底改变基于结构的药物设计.
- 对SBDD的DL的持续研究预计将加速新疗法的发现.
- DL分析复杂模式的能力在预测分子相互作用和性质方面提供了显著的优势.
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