Towards accelerating small molecule drug discovery with pre-trained, late fusion multi-view models

Foundation models have transformed the execution of many tasks and are an area of active exploration in small molecule drug discovery.  Typically, small molecule foundation models focus on a single representation of the molecule, such as a SMILES string input into a text-based model.  However, molecules may be represented in numerous ways including as images, chemically bonded graphs, or three-dimensional structures.  Each representation or ‘view’ contains different, potentially complementary information that if combined can yield a more accurate and robust model.  Here we describe a multi-view foundation model that incorporates several pre-trained representations to achieve this goal.  Each view has already been pre-trained on hundreds of millions of molecules.  Complementarity of representations in embedding space is evaluated.  We explore multi-modal, late fusion techniques and fine-tune our models on datasets covering a large variety of downstream tasks.  We find that our multi-view models can overall outperform models reliant on a single representation.