Machine Learning-Guided Strategy Selection and Condition Prediction for Photoredox Catalysis

Abstract Photoredox catalysis offers powerful opportunities for organic synthesis, yet its broader application remains limited by the difficulty of identifying viable synthetic routines and conditions. Here, we present an experimentally validated, machine-learning-assisted workflow that combines literature-precedent retrieval with reaction-condition recommendation to support chemist-guided design of photoredox transformations. With 11,069 curated literature-derived reaction records, a variational autoencoder organizes photoredox reactions in a learned latent space to enable reaction-space navigation and retrieval of traceable synthetic precedents, while random-forest classifiers recommend feasible catalyst and reagent classes for proposed reactant–product pairs. External validation on unseen literature examples and experimental validation of selected model-guided proposals demonstrates the practical utility of the workflow. It further enabled three unreported photoredox transformations, highlighting its possibility for generating experimentally testable synthetic hypotheses.