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54 papers
This work introduces function-aware masking, a family of pretraining algorithms that align mask placement with specific functional priors (e.g., from IMGT annotations or structure predictions) to shape the learned representation space, and demonstrates that informed mask placement provides a parameter-free mechanism fo...
Frequency-Domain Latent-attention Gated Pooling (FLaG), a plug-in aggregation module that re-expresses encoder outputs in the Fourier domain before final pooling, provides a transferable frequency-domain aggregation bias across protein, visual, and textual representations, with benefits that depend on the backbone and...
A simple, sequence-only pipeline can match and surpass leading methods by combining 330 interpretable sequence descriptors with TabPFN, a tabular foundation model that performs in-context prediction in a single forward pass without gradient-based training or hyperparameter search.
Results show that explicitly teaching the relation between a molecule and its structural core can reliably shape the organization of molecular embedding space, while the extent of usefulness of this organization remains task dependent.
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EquiPocket is proposed, an E(3)-equivariant Graph Neural Network for binding site prediction, which comprises three modules: the first one to extract local geometric information for each surface atom, the second one to model both the chemical and spatial structure of protein and the last one to capture the geometry of...
GEqTrain is presented, a configuration-driven framework that separates dataset semantics, model composition, and training objectives, and GEqDiff, a generative extension based on equivariant flow matching that aims to make equivariant modeling more reproducible, extensible, and reusable.
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