主动学习FEP:对AL协议和化学多样性的性能的影响
Richard Lonsdale1, Jack Glancy1, Leen Kalash1
1GSK, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
Journal of chemical theory and computation
|April 17, 2025
概括
使用自由能量扰动 (AL-FEP) 的积极学习有效地构建药物发现的预测模型. 这种方法可以快速生成准确的生化功率预测,特别是当保持恒定的分子核心时.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
背景情况:
- 使用自由能量扰动 (AL-FEP) 的积极学习是开发机器学习模型来预测生化功能的策略.
- 这种方法在早期的优化中特别有用,当实验数据稀缺时.
研究的目的:
- 评估AL-FEP在生成代抑制剂系列预测模型中的性能.
- 以回顾分析AL-FEP工作流程参数,以便在药物发现项目中常规应用.
主要方法:
- AL-FEP应用于两种不同的代抑制剂系列,不同的是核心结构是不变的还是包含在化合物想法生成中.
- 模型性能使用测量生物化学效能数据进行评估.
- 一次回顾性评估涵盖了化合物选择策略,探索-开发比率和基于循环的化合物数量.
主要成果:
- 在几个活跃的学习周期内生成了表现良好的预测模型,特别是当核心结构保持不变时.
- 根据工作流参数观察到显著的性能变化 (模型丰富,R平方).
- 该研究根据部署背景 (功率最大化与广泛准确性) 确定了AL-FEP的特定参数建议.
结论:
- AL-FEP是一种可行的方法,用于在早期药物发现中生成预测模型.
- 优化AL-FEP工作流参数对于最大限度地提高模型性能和实现项目特定目标至关重要.
- 这些发现为AL-FEP在药物化学项目中的常规实施提供了指导.
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