贝叶斯优化在药物发现中的贝叶斯优化
Lionel Colliandre1, Christophe Muller2
1Evotec SAS (France), Toulouse, France. lionel.colliandre@evotec.com.
Methods in molecular biology (Clifton, N.J.)
|September 13, 2023
概括
贝叶斯优化 (BO) 通过优化候选人配置文件来加速药物发现. 这种计算方法完善了试错方法,以实现更快,更高效的药物设计.
科学领域:
- 计算化学的计算化学
- 药物设计 药物设计
- 机器学习在药理学中的应用
背景情况:
- 药物发现涉及候选分子的代优化.
- 当前的优化方法通常依赖于广泛的试错过程.
- 在的方法对于加速药物发现管道至关重要.
研究的目的:
- 引入贝叶斯优化 (BO) 作为一种强大的药物设计工具.
- 解释用于药物发现的BO的原理和算法组件.
- 突出在药物发现制约条件下对BO的实际应用和解决方案.
主要方法:
- 在药物设计中探索黑子优化概念.
- 贝叶斯优化原理和算法的详细解释.
- 专注于使BO适应制药研究的特定约束.
主要成果:
- 证明BO在确定候选药物的最佳功能方面的有效性.
- 介绍了在药物发现项目中实施BO的可访问解释.
- 汇编了BO在现场的各种实际应用.
结论:
- 贝叶斯优化在药物发现中比传统的试错方法提供了显著的进步.
- BO提供了一个强大的框架,可以有效地优化候选药物.
- 该章节为研究人员提供了在药物设计工作中应用BO的知识.
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