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.
By Shan Yu, Xuening Wu
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...
By Jae Won Choi, Ryoonki Hong, Alan Liang, Manjula Adiveppa Wader, Bingsong Zeng, Peiyang Tang, Longwei Liu, Ruishan Liu
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....
By Siyuan Chen, Cai Zhou, Jinrui Zhang, Zhaokang Liang, Taku Komura, Wojciech Matusik, Stephen Bates, Tommi Jaakkola, Wengong Jin, Peter Yichen Chen, Minghao Guo
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...
By Lisheng Zhang, Qingsong Li, Zinan Xie, Yanting Yang, Qikui Yang, Peng Lu, Zhican Fu, Wentao Cui, Runtong Hou, Zhengwei Xie
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.
By Qingyang Zou, Jiaye Huang, Hangbo Xie, Jiayue Yin, Youyi Song, Jinfeng Liu
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.
By Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son Hy
SurfSpec is a lead‑optimization framework that improves drug specificity without needing off‑target structures. By measuring and reducing the geometric mismatch between a ligand and its target pocket, SurfSpec provides a conservative lower bound on specificity against geometrically separated off‑target pockets. The method iteratively grows ligands toward under‑occupied target surface patches, alternating between linker generation and refinement, and demonstrates superior empirical specificity on the CrossDocked2020 test set while maintaining competitive target affinity.
arXiv:2606. 04020v1 Announce Type: cross Abstract: Splice-mediated drug resistance occurs in up to 40% of patients on targeted kinase inhibitors, yet state-of-the-art druggability tools operate on single structures and cannot compare across isoforms.
By Bryan Cheng, Austin Jin, Joshua Chang
HierDTA is a hierarchical structure‑aware framework for drug–target affinity prediction that represents drugs as atom–motif heterogeneous graphs and proteins as residue interaction graphs. It integrates atom and residue representations via cross‑attention, supplemented by molecule fingerprints and pretrained ESM‑2 embeddings, achieving superior performance on the Davis and KIBA datasets and strong generalization in cold‑start scenarios. Ablation studies and interpretability analyses confirm the contributions of its hierarchical augmentation and interaction components, and case studies demonstrate early enrichment of candidate bioactive compounds.
By Huangchao Xu, Baohua Zhang, Qian Liu, Zhong Jin, Yi Du
SurfSpec is a new framework for lead optimization that improves drug specificity without needing off‑target structures. It works by measuring and reducing the geometric mismatch between a ligand and its target pocket, using the triangle inequality to infer a lower bound on mismatch to off‑target pockets. In tests on the CrossDocked2020 dataset, SurfSpec lowers geometric mismatch and achieves higher empirical specificity while still improving target affinity.
By Minyeong Hwang, Yoorim Gang, Ziseok Lee, Wooyeol Lee, Young Bin Park, Jae-Mun Choi, Kyungsu Kim, Eunho Yang
arXiv:2607. 19237v1 Announce Type: new Abstract: Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics.
By Yiming Qin, Kai Yi, Miruna Cretu, Sjors H. W. Scheres, Pietro Li\`o, Pascal Frossard
arXiv:2607. 17601v1 Announce Type: cross Abstract: Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust.
By Yongchan Hong, Defu Cao, Wenjin Liu, Thomas Ku, Jordy Homing Lam, Emily Nguyen, Willie Neiswanger, Vsevolod Katritch, Yan Liu