Explainable Machine Learning for Property Predictions in Compound Optimization,Journal of Medicinal Chemistry

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Explainable Machine Learning for Property Predictions in Compound Optimization
Journal of Medicinal Chemistry ( IF 6.8 ) Pub Date : 2021-12-13 , DOI: 10.1021/acs.jmedchem.1c01789
Raquel Rodríguez-Pérez _{1,

2} , Jürgen Bajorath ₁

Affiliation

The prediction of compound properties from chemical structure is a main task for machine learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications such as compound screening, virtual library enumeration, or generative chemistry. Albeit desirable, a detailed understanding of ML model decisions is typically not required in these cases. By contrast, compound optimization efforts rely on small data sets to identify structural modifications leading to desired property profiles. In this situation, if ML is applied, one usually is reluctant to make decisions based on predictions that cannot be rationalized. Only few ML methods are interpretable. However, to yield insights into complex ML model decisions, explanatory approaches can be applied. Herein, methodologies for better understanding of ML models or explaining individual predictions are reviewed and current challenges in integrating ML into medicinal chemistry programs as well as future opportunities are discussed.

中文翻译：

复合优化中属性预测的可解释机器学习

从化学结构预测化合物性质是药物化学中机器学习 (ML) 的主要任务。ML 通常应用于化合物筛选、虚拟库枚举或生成化学等应用中的大型数据集。尽管可取，但在这些情况下通常不需要详细了解 ML 模型决策。相比之下，复合优化工作依赖于小数据集来识别导致所需属性配置文件的结构修改。在这种情况下，如果应用机器学习，人们通常不愿意根据无法合理化的预测做出决策。只有少数 ML 方法是可解释的。但是，为了深入了解复杂的 ML 模型决策，可以应用解释性方法。在此处，

更新日期：2021-12-23

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