Multi-View Biomedical Foundation Models for Molecule-Target and Property Prediction.

Molecular foundation models hold promise to provide accurate predictions for a large and diverse set of downstream tasks in bio-medical research. Quality molecular representations are key and foundation model development has typically focused on a single representation or molecular view, which may have strengths or weaknesses on a given task. We develop Multi-view Molecular Embedding with Late Fusion (MMELON), an approach that integrates pre-trained graph, image and text foundation models and may be readily extended to additional views and models. The multi-view model performs robustly and is validated on over 120 tasks, including molecular solubility, ADME properties, and activity against G Protein-Coupled receptors (GPCRs). The GPCR model array is leveraged to perform a virtual screen in search of ligands binding to Alzheimer's disease related GPCRs. We identify a number of such targets and employ the multi-view model to select strong binders from a compound screen. Predictions are validated through structure-based modeling and identification of key binding motifs.