Re-Evaluating Chemical Synthesis Planning Algorithms - Tripp, Austin; Maziarz, Krzysztof; Lewis, Sarah; Liu, Guoqing; Segler, Marwin (NeurIPS 2022 (AI for Science: Progress and Promises Workshop), 2022)
Computer-Aided Chemical Synthesis Planning (CASP) algorithms have the potential to help chemists predict how to make molecules, and decide which molecules to prioritize for synthesis and testing. Recently, several algorithms have been proposed to tackle this problem, reporting large performance improvements. In this work, we re-examine current and prior State-of-the-Art synthesis planning algorithms under controlled and identical conditions, providing a holistic view using several previously un-reported evaluation metrics which cover the common use-cases of these algorithms. In contrast to prior studies, we find that under strict control, differences between algorithms are smaller than previously assumed. Our findings can guide users to choose the appropriate algorithms for specific tasks, as well as stimulate new research in improved algorithms.