Article

Protein Language Models and Structure-Based Machine Learning for Prediction of Allosteric Binding Sites in Protein Kinases: An Explainable AI Framework Grounded in Energy Landscape-Encoded Frustration

2026-01-06

Abstract excerpt

Reliable identification of allosteric binding sites remains a major bottleneck in structure-based drug discovery, particularly in protein kinase families where such sites are often structurally cryptic, evolutionarily non-conserved, and sparsely populated. In this work, we present a systematic analysis of binding site prediction across a rigorously curated dataset of human kinase–ligand complexes, encompassing 453...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 51155f36-ce8e-56c4-b2f6-4bf87ed706eb
DOI: 10.64898/2026.01.05.697819

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Protein Language Models and Structure-Based Machine Learning for Prediction of Allosteric Binding Sites in Protein Kinases: An Explainable AI Framework Grounded in Energy Landscape-Encoded FrustrationDOI 10.64898/2026.01.05.697819Read

Decoding the Allosteric Paradox: A Dual Framework Integrating AI Cofolding Models with Landscape-Guided Interpretable AI Framework of Ligand-Protein BindingSemantic proximity Read

HTS-Oracle: Experimentally validated AI-enabled prioritization for generalizable small molecule hit discoverySemantic proximity Read

Pretrained protein language models choose between sequence novelty and structural completenessSemantic proximity Read

Integrative machine learning predicts activating kinase mutations for precision oncologySemantic proximity Read

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.Semantic proximity Read

Allo-Allo: Data-efficient prediction of allosteric sitesSemantic proximity Read

Improving Variant Effect Prediction by Steering Sparse Mechanistic Features in Protein Language ModelsSemantic proximity Read

Structure-free, site-resolved contrastive learning extends small-molecule discovery beyond the reach of structure-based modelingSemantic proximity Read

A Comprehensive Atlas and Machine-Learning Framework for Predicting IDR-Protein Binding AffinitySemantic proximity Read

Dynamics-aware geometric learning predicts disease-associated molecular perturbationsSemantic proximity Read

Applying Protein Language Models Using Limited Dataset. Sequence-Based Hot Spot Prediction in Protein Interactions Using AutoGluonSemantic proximity Read

MetalPrognosis: a Biological Language Model-based Approach for Disease-Associated Mutations in Metal-Binding Site predictionSemantic proximity Read