Towards out-of-distribution generalizable predictions of chemical kinetics properties
This work addresses the need for OOD-generalizable predictions in chemical kinetics to support high-throughput synthesis processes, but it is incremental as it focuses on benchmarking and dataset creation rather than introducing a new method.
The paper tackles the problem of out-of-distribution (OOD) generalization in predicting chemical kinetics properties for AI-driven chemical synthesis, by categorizing OOD challenges into three levels and benchmarking existing ML methods, revealing both challenges and opportunities.
Machine Learning (ML) techniques have found applications in estimating chemical kinetic properties. With the accumulated drug molecules identified through "AI4drug discovery", the next imperative lies in AI-driven design for high-throughput chemical synthesis processes, with the estimation of properties of unseen reactions with unexplored molecules. To this end, the existing ML approaches for kinetics property prediction are required to be Out-Of-Distribution (OOD) generalizable. In this paper, we categorize the OOD kinetic property prediction into three levels (structure, condition, and mechanism), revealing unique aspects of such problems. Under this framework, we create comprehensive datasets to benchmark (1) the state-of-the-art ML approaches for reaction prediction in the OOD setting and (2) the state-of-the-art graph OOD methods in kinetics property prediction problems. Our results demonstrated the challenges and opportunities in OOD kinetics property prediction. Our datasets and benchmarks can further support research in this direction.