Domain-aware representation of small molecules for explainable property prediction models

The advances in deep learning algorithms have impacted the drug discovery pipeline in many ways. Specifically, generative artificial intelligence (AI) algorithms can now explore the large chemical space and design novel and diverse molecules. While there has been significant progress in generative AI models, it is equally important to develop predictive models for various properties, which can help to characterize novel drug-like molecules. Further, the predictive model acts as a critic to design multi-property optimized molecules, which can potentially reduce the late stage attrition of drug candidates. Nevertheless, understanding the reason behind model predictions can guide the medicinal chemist to modify substructures that can make the molecules undesirable during the lead optimization stage of drug discovery. However, current explainable approaches are mostly atom-based where, often only a fraction of a fragment is shown to be significant. To address the above challenges, we have developed a novel domain-aware fragment-based graph input representation based on a molecular fragmentation approach termed pBRICS, which can fragment small molecules into their functional groups. Both single and multi-task models were developed to predict various properties including ADMET properties. The fragment level explainability were obtained using the Grad-CAM approach. The method was further validated with the available Matched Molecular Pairs (MMP) for blood brain barrier permeability (BBBP) and Ames Mutagenicity.