Improving reaction prediction through chemically aware transfer learning

Abstract

Practical applications of machine learning (ML) to new chemical domains are often hindered by data scarcity. Here we show how data gaps can be circumvented by means of transfer learning that leverages chemically relevant pre-training data. Case studies are presented in which the outcomes of two classes of pericyclic reactions are predicted: [3,3] rearrangements (Cope and Claisen rearrangements) and [4 + 2] cycloadditions (Diels–Alder reactions). Using the graph-based generative algorithm NERF, we evaluate the data efficiencies achieved with different starting models that we pre-trained on datasets of different sizes and chemical scope. We show that the greatest data efficiency is obtained when the pre-training is performed on smaller datasets of mechanistically related reactions (Diels–Alder, Cope and Claisen, Ene, and Nazarov) rather than >50× larger datasets of mechanistically unrelated reactions (USPTO-MIT). These small bespoke datasets were more efficient in both low re-training and low pre-training regimes, and are thus recommended alternatives to large diverse datasets for pre-training ML models.