Multi-view biomedical foundation models for molecule-target and property prediction

Foundation models applied to bio-molecular space hold promise to accelerate drug discovery. Molecular representation is key to building such models. Previous works have typically focused on a single representation or view of the molecules. Here, we develop a multi-view foundation model approach, that integrates molecular views of graph, image and text. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules and then aggregated into combined representations. Our multi-view model is validated on a diverse set of 18 tasks, encompassing ligand-protein binding, molecular solubility, metabolism and toxicity. We show that the multi-view models perform robustly and are able to balance the strengths and weaknesses of specific views. We then apply this model to screen compounds against a large (>100 targets) set of G Protein-Coupled receptors (GPCRs). From this library of targets, we identify 33 that are related to Alzheimer's disease. On this subset, we employ our model to identify strong binders, which are validated through structure-based modeling and identification of key binding motifs.

@article{suryanarayanan2025_2410.19704,
  title={ Multi-view biomedical foundation models for molecule-target and property prediction },
  author={ Parthasarathy Suryanarayanan and Yunguang Qiu and Shreyans Sethi and Diwakar Mahajan and Hongyang Li and Yuxin Yang and Elif Eyigoz and Aldo Guzman Saenz and Daniel E. Platt and Timothy H. Rumbell and Kenney Ng and Sanjoy Dey and Myson Burch and Bum Chul Kwon and Pablo Meyer and Feixiong Cheng and Jianying Hu and Joseph A. Morrone },
  journal={arXiv preprint arXiv:2410.19704},
  year={ 2025 }
}