This research introduces a novel approach to integrate large language models from different biological domains to improve drug design through better prediction of biophysical interactions.

• Combines protein and small molecule language models to capture complex molecular binding mechanisms
• Addresses critical limitations in current AI approaches to virtual screening
• Creates more comprehensive representations of molecular complexes by fusing different biological modalities
• Enhances ability to predict binding affinity and specificity - key factors in drug efficacy

Medical Impact: By better modeling the molecular interactions crucial for drug binding, this approach promises to significantly accelerate pharmaceutical development, reduce false positives in screening, and ultimately deliver more effective therapeutics with fewer side effects.

Two for the Price of One: Integrating Large Language Models to Learn Biophysical Interactions