arXiv AI
zPocket: Better Pocket Finding with Frozen Co-Folding Features
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arXiv Machine Learning
Oct 3
Auditable Algebraic Counting Field for Cryptic-Pocket Detection from Apo Structures
The paper introduces a supervised algebraic counting field (ACF) that predicts cryptic ligand‑binding pocket residues from apo protein structures by compiling geometric, physicochemical, and topological features into compact integer‑weighted lookup tables. ACF allows each prediction score to be reconstructed from feature values, training counts, table weights, and spatial aggregation, eliminating the need for sequence search, structural‑template transfer, or a protein language model during inference. In benchmark tests on CryptoBench and external datasets, ACF outperformed the P2Rank method in mean paired ROC‑AUC on one external set, though gains varied across datasets and were not universally confirmed.
arXiv Machine Learning
Oct 8
PIT-GCL: Protein Interaction using Topological Graph Contrastive Learning
arXiv:2610.04850v2 Announce Type: replace Abstract: Protein binding prediction is central to target identification, therapeutic binder design, and large scale screening, yet remains challenging becau...
arXiv AI
Sep 30
From Surfaces to Volumes: Registered Geometry for Protein Representation Learning
arXiv:2609.36277v1 Announce Type: new Abstract: Existing protein geometry models typically represent molecular surfaces using local geometric features such as sampled points, normals, and curvature....
arXiv AI
3d ago
Predicting protein structures at any folding stage from sequence
arXiv:2611.04287v2 Announce Type: replace-cross Abstract: Protein structure prediction has largely focused on native conformations, leaving the continuum of unfolded states, folding intermediates, an...
arXiv Machine Learning
Aug 11
RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction
arXiv:2608. 09099v1 Announce Type: new Abstract: Quantitative estimation of protein-ligand binding affinity from three-dimensional complex structures is a fundamental task in structure-based computational chemistry and molecular modeling.
arXiv Machine Learning
Jul 15
PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.