Challenging Reaction Prediction Models to Generalize to Novel Chemistry.

ABSTRACT: Deep learning models for anticipating the products of organic reactions have found many use cases, including validating retrosynthetic pathways and constraining synthesis-based molecular design tools. Despite compelling performance on popular benchmark tasks, strange and erroneous predictions sometimes ensue when using these models in practice. The core issue is that common benchmarks test models in an in-distribution setting, whereas many real-world uses for these models are in out-of-distribution settings and require a greater degree of extrapolation. To better understand how current reaction predictors work in out-of-distribution domains, we report a series of more challenging evaluations of a prototypical SMILES-based deep learning model. First, we illustrate how performa

SUBMITTER: Bradshaw J 

PROVIDER: S-EPMC12022916 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature