VQ-Atom discretizes local atomic environments into semantic tokens via vector quantization, reaching AUROC 0.79 on KIBA drug-target interaction prediction while enabling 3x faster downstream training than continuous representations.
Neural machine translation of rare words with subword units
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VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning
VQ-Atom discretizes local atomic environments into semantic tokens via vector quantization, reaching AUROC 0.79 on KIBA drug-target interaction prediction while enabling 3x faster downstream training than continuous representations.