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Mol-JEPA advances molecular foundation models by addressing key limitations like modality collapse through a multimodal Joint Embedding Predictive Architecture. By integrating diverse data sources including quantum chemistry simulations and ADMET profiles, the framework creates robust latent representations for drug discovery tasks. This approach offers a scalable solution to improve biochemical context understanding beyond traditional perturbation-based methods.
Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules
arXiv:2608.22642v1 Announce Type: cross Abstract: Despite recent advances in molecular foundation models, several limitations remain, such as chemically invalid augmentations, modality collapse, and incomplete representation of biochemical environments. To address these challenges, we present \textbf{Mol-JEPA}, a scalable framework for learning molecular world models. Rather than relying on suboptimal molecular perturbations, our model uses modality masking to exploit information from molecular structures, cellular phenotypes, binding affinities, ADMET profiles, quantum chemistry simulations and other drug discovery data. Across various benchmarks, we show that the representations learned by Mol-JEPA deliver strong performance, demonstrating the value of incorporating biochemical context through latent space prediction.
Abstract:Despite recent advances in molecular foundation models, several limitations remain, such as chemically invalid augmentations, modality collapse, and incomplete representation of biochemical environments. To address these challenges, we present \textbf{Mol-JEPA}, a scalable framework for learning molecular world models. Rather than relying on suboptimal molecular perturbations, our model uses modality masking to exploit information from molecular structures, cellular phenotypes, binding affinities, ADMET profiles, quantum chemistry simulations and other drug discovery data. Across various benchmarks, we show that the representations learned by Mol-JEPA deliver strong performance, demonstrating the value of incorporating biochemical context through latent space prediction.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2608.22642 [cs.LG]
(or arXiv:2608.22642v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.22642 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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