Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models

Significant advances have been made in the in silico prediction of molecular and pharmacokinetic properties associated with successful drug-like molecules (Leeson et al., 2021; Lombardo et al., 2017). These cheminformatics advances have laid the foundation for further enhancements in drug candidate screening, prioritization for advancement into in vivo studies, and clinical candidate selection (Maurer et al., 2021). Despite these impressive improvements in molecular property predictions, a considerable challenge remains in accurately predicting the affinity/potency of a ligand-protein interaction (LPI), also known as a drug-target interaction (DTI) (Yamanishi et al., 2008). Drugs convey their phenotypic effects through interactions with a variety of biological targets with varying affinities (Swinney & Anthony, 2011). Some interactions produce desirable outcomes and phenotypes, while others can create undesired side effects and/or safety risks (Waring et al., 2015). Accurately predicting the affinities of ligand-protein interactions would enable drug discovery teams to better design and prioritize the synthesis of molecules that interact with intended protein targets, while minimizing undesired interactions with off-targets like hERG and liver enzymes, ultimately increasing the chances of preclinical success.