在超大规模时代,以高性能为导向的计算机辅助药物设计方法
Andrea Rizzi1,2, Davide Mandelli1
1Computational Biomedicine (INM-9), Forschungszentrum Jülich Gmbh, Wilhelm-Johnen Straße, Jülich, Germany.
Expert opinion on drug discovery
|February 15, 2025
概括
超级计算为计算机辅助药物设计 (CADD) 提供了新的可能性. 先进的基于物理和机器学习的方法现在可以扩展到设计新的小分子结合剂,加速治疗开发.
科学领域:
- 计算科学是一种计算科学.
- 药物发现 药物发现
- 高性能计算 (HPC) 是一种高性能计算.
背景情况:
- 超级计算的出现,以边界系统为例,标志着计算能力的重大进步.
- 超大规模计算为计算机辅助药物设计 (CADD) 等领域带来革命性的机会.
- 将现有的CADD方法扩展到超级架构需要新的算法和软件解决方案.
研究的目的:
- 探索基于物理和机器学习 (ML) 辅助技术的应用,用于在超大规模系统上设计小分子绑定器.
- 审查过去三年的以HPC为导向的大规模CADD应用程序,这些应用程序使用了超级计算机.
- 确定利用超大规模计算用于先进药物设计的潜在和当前局限性.
主要方法:
- 最近 (3年) 在CADD中面向HPC的大规模应用的审查.
- 分析适用于并行计算机架构的基于物理的方法.
- 机器学习 (ML) 方法的评估,包括生成模型和基于物理的ML辅助方法.
主要成果:
- 超大规模计算可以使高预测性生成模型用于新型联体设计的训练成为可能,如果有足够的数据.
- 精确的基于物理的ML辅助方法在超级系统上显示出增强基于结构的药物设计成功率的前景.
- 对于这些严格的CADD方法的常规,大规模应用,目前的方法进步仍然是必要的.
结论:
- 超大规模计算在计算机辅助药物设计方面具有变革性的潜力.
- 在超大尺度平台上整合基于物理和ML的方法可以加速发现小分子结合物的发现.
- 进一步的方法开发至关重要,以充分实现超级尺度驱动药物设计的好处.
相关概念视频
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00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
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