Title: Domain Knowledge Infused Conditional Generative Models for Accelerating Drug Discovery
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Drug Discovery, AI4Science, domain knowledge, conditional generative models, diffusion models

Abstract: The role of Artificial intelligence (AI) is growing in every stage of drug development. Nevertheless, a major challenge in drug discovery AI remains: Drug pharmacokinetic (PK) and Drug-Target Interaction datasets collected in different studies often exhibit limited overlap, creating data overlap sparsity. Thus, data curation becomes difficult, negatively impacting downstream research investigations in high-throughput screening, polypharmacy, and drug combination. We propose xImagand-DKI, a novel SMILES/Protein-to-Pharmacokinetic/DTI diffusion model capable of generating an array of PK and DTI target properties conditioned on SMILES and protein inputs that exhibit data overlap sparsity. We infuse additional molecular and genomic domain knowledge from the Gene Ontology and molecular fingerprints to further improve our model performance. We show that xImagand-DKI generates synthetic PK data that closely resemble real data univariate and bivariate distributions, and can adequately fill in gaps among PK and DTI datasets. As such, xImagand-DKI is a promising solution for data overlap sparsity and may improve performance for downstream drug discovery research tasks. Our code and data are available open-source: TBD.