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, tha...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2410.19704 |