Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors
Yanfei Guan, Connor W. Coley, Haoyang Wu, Duminda S. Ranasinghe, Esther Heid, Thomas J. Struble, Lagnajit Pattanaik, William H. Green, Klavs F. Jensen
Abstract
calculations of 130k organic molecules, and train a multi-task constrained model to calculate demanded descriptors on-the-fly. The proposed platform enhances the inter/extra-polated performance for regio-selectivity predictions and enables learning from small datasets with just hundreds of examples. Furthermore, the proposed protocol is demonstrated to be generally applicable to a diverse range of chemical spaces. For three general types of substitution reactions (aromatic C-H functionalization, aromatic C-X substitution, and other substitution reactions) curated from a commercial database, the fusion model achieves 89.7%, 96.7%, and 97.2% top-1 accuracy in predicting the major outcome, respectively, each using 5000 training reactions. Using predicted descriptors, the fusion model is end-to-end, and requires approximately only 70 ms per reaction to predict the selectivity from reaction SMILES strings.