以几何深度学习为指导的苏子基反应条件评估,用于药物化学中的应用
Kenneth Atz1, David F Nippa1, Alex T Müller1
1Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland kenneth.atz@roche.com georg.wuitschik@roche.com.
RSC medicinal chemistry
|July 19, 2024
概括
在高通量实验 (HTE) 数据上训练的机器学习模型加速了苏苏基交叉合反应条件选择,用于药物发现. 少数射击学习模型显示出优化HTE活动的希望,提高合成新型化合物的效率.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 有机合成 有机合成
背景情况:
- 木交叉合反应对于在小分子药物发现中形成碳-碳键至关重要.
- 化学合成通常是由于时间和劳动强度的瓶.
研究的目的:
- 为了利用机器学习 (ML) 在高通量实验 (HTE) 数据上训练的快速反应条件选择.
- 为了使化学家能够有效地确定新型合伙伴的最佳催化剂-溶剂-基组合.
- 评估零射击和少数射击ML模型在预测反应成功方面的性能.
主要方法:
- 开发在HTE数据上训练的ML模型,用于反应条件预测.
- 实现设计96井板的算法,以最大限度地提高反应产量.
- 对零射击和少数射击ML模型性能进行比较分析.
主要成果:
- 最好的几次射击的ML模型在三类分类中获得了76.3%的准确性,在二元分类中获得了79.1%的F1分数.
- 少数射击ML表现出强的性能,平均ROC-AUC为0.82 (±0.07) 在八种反应中.
- 零射击ML模型实现了0.63 (±0.16) 的较低平均ROC-AUC.
结论:
- 少数射击ML引导的反应条件选择是医药化学中HTE运动的有价值的方法.
- 这一策略加速了针对新型化合物的苏苏基交叉合反应的优化.
- 在此背景下,零射击ML的挑战和实际应用也得到了强调.
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