Abstract

Predicting aromatic substitution sites for new molecules remain a challenge with large industry demand as its products have a myriad of applications. Classical methods involve rule-based approaches to ab initio methods that scale in computational time for more complex scenarios of heteroaromatic and multi-substituted systems. Previous works have explored ab initio, as well as hybrid methods with bespoke descriptors for each reaction site (86% accuracy, average 2,899 ms/inference). Here, we explore a data-driven model for halogenation site-selectivity achieving 80% accuracy with average 43 ms/inference. Our architecture combines machine learning with molecular fingerprints and algorithmic manipulation of chemical scaffolds. We also present an exploration of how different datasets – chlorination, bromination, and iodination – can be combined into a superset to increase prediction power of the final model. Finally, model performance is higher when compared to chemist, as they have through knowledge of scaffolds they have previously worked with. This model compared to chemists. Although the sample size is small, those working on the chemical industry have deep knowledge on certain molecular scaffolds while fast and accurate models can extend their reach to new areas.