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Department of Data Science , University of San Francisco , CA 94117 , USA
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Topological spectral representations and latent interaction attention unlock structured, source-target-dependent information exchange for biomolecular learning, achieving SOTA drug discovery performance.
Unlocking superior molecular and materials property prediction by fusing diverse mathematical invariants with topological neural networks, revealing that optimal performance hinges on carefully curated representation-architecture pairings.