Computer-Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions

Computer-aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain. This paper reviews the topic of the Computer Aided Synthesis Project (CASP) and gives a detailed overview of the methods of machine learning to predict chemical reaction conditions, including the problem of data and descriptor selection, the indicators for the evaluation of results and the current research progress of various algorithms. The future research trends and possible challenges in chemical condition prediction are prospected. image