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Unified atomistic modeling has the potential to accelerate discovery in chemistry, materials science, and biology by bridging data-rich chemical domains and data-scarce biological contexts. However, existing generative approaches to atomistic modeling remain highly specialized to scientific disciplines (chemistry vs. b...
Riemannian Wasserstein Entropic Flow Matching (RWEFM) is introduced, a generative framework on the Wasserstein space of a Riemannian manifold that can generate whole single-cell samples in hyperspherical latent spaces and protein conformational ensembles on the torus by respecting the intrinsic geometry of the data.
The ImmunoFoundation Model (IFM), a multimodal deep learning system that integrates not only peptide sequences, 3D molecular structures, and biochemical properties but also TCR-MHC-peptide to achieve superior immunogenicity prediction and enable peptide optimization for therapeutic applications is developed.
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