Molecular Machine Learning for Asymmetric (Organo-)Catalysis

不对称催化在药物活性化合物的合成中起着关键作用。有机催化的目的是在不使用潜在有毒金属的情况下实现这种转化。然而，其最大的挑战在于设计有效的有机催化剂和预测活性：在大多数情况下，催化剂的分子结构与其活性之间的相关性知之甚少。因此，我们将开发机器学习（ML）技术并将其应用于硫脲和寡肽催化剂库：我们的目标是确定最可行的候选药物的有机催化转化，如抗疟疾剂的合成。通过在我们的ML模型上使用可解释的AI技术，我们计划揭示负责催化剂活性的分子特征，以进一步实现真正的从头催化剂设计的目标。

Asymmetric catalysis plays a pivotal role in the synthesis of pharmaceutically active compounds. Organocatalysis aims to enable such transformations without the use of potentially toxic metals. Its biggest challenge, however, lies in the design of potent organocatalysts and prediction of activity: In most cases, the correlation between a catalyst's molecular structure and its activity is poorly understood. Thus, we will develop and apply machine learning (ML) techniques to thiourea and oligopeptide catalyst libraries: We aim to determine the most viable candidates for organocatalytic transformations of pharmaceutical interest, such as for the synthesis of anti-malarial agents. By using explainable AI techniques on our ML models, we plan to reveal the molecular features responsible for catalyst activity to move further towards the goal of true de novo catalyst design.