药物设计中的物理学-人工智能对话
Pablo Andrés Vargas-Rosales1, Amedeo Caflisch1
1Department of Biochemistry, University of Zurich Winterthurerstrasse 190 8057 Zürich Switzerland caflisch@bioc.uzh.ch.
RSC medicinal chemistry
|February 5, 2025
概括
机器学习 (ML) 在几十年的研究基础上,推动了蛋白质科学和药物发现. 了解ML的优势和局限性是将其与基于物理的方法协同用于最佳药物设计的关键.
科学领域:
- 生物化学 生物化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 自1960年以来,蛋白质结构的确定已经显著发展.
- 蛋白质科学中的机器学习 (ML) 方法得到了2024年诺贝尔化学奖的认可.
- 机器学习应用越来越多地被整合到药物发现工作流程中.
研究的目的:
- 审查ML在蛋白质科学和药物发现中的演变和当前状态.
- 突出 ML 的挑战和新兴应用.
- 强调ML和基于物理的方法之间的协同作用.
主要方法:
- 在蛋白质结构预测和设计中对机器学习技术的审查.
- 分析ML在药物发现中的应用,包括姿势评分和分子描述器生成.
- 讨论ML与基于物理的方法 (如分子动力学模拟) 之间的互补性.
主要成果:
- 机器学习工具在混合物之间进行插入,以优化击中到领先的过程中表现出有希望.
- 基于物理学的方法,如自由能量计算,对于新型衍生设计来说似乎更优越.
- 基于物理和ML技术之间存在着日益增长的协同作用.
结论:
- 机器学习已经彻底改变了蛋白质科学,但在预测结构组合方面仍然存在挑战.
- 将机器学习的潜在好处与环境成本相平衡至关重要.
- 对ML优势和局限性的跨学科理解对于利用其与基于物理的方法在药物设计中的协同作用至关重要.
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