Article

Beyond Random Splits: A Critical Evaluation of Graph Learning Models in Predicting Mutation-Induced Drug Resistance

2026-04-02

Abstract excerpt

<h4>Background: </h4> Deep learning has become an important tool for predicting mutation-induced changes in binding free energy (ΔΔG). However, most current state-of-the-art methods rely heavily on paired wild-type (WT) and mutant (MT) complex structures during both training and inference. This dependence on post-mutation structural information substantially limits their practical utility in real-world scenarios,...

Topics

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

Identifiers and source

Literature Corpus work: ae64fad3-004b-5354-8257-882450e12d96
DOI: 10.20944/preprints202604.0069.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Beyond Random Splits: A Critical Evaluation of Graph Learning Models in Predicting Mutation-Induced Drug ResistanceDOI 10.20944/preprints202604.0069.v1Read

Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Semantic proximity Read

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation.Semantic proximity Read

Transfer learning across molecular graphs for predicting protein-ligand affinities and their changes upon mutationsSemantic proximity Read

MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Semantic proximity Read

Sequence-aware Prediction of Point Mutation-induced Effects on Protein-Protein Binding Affinity using Deep LearningSemantic proximity Read

DeepPNI: a language- and graph-based model for mutation-driven protein-nucleic acid binding energetics.Semantic proximity Read

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.Semantic proximity Read

Emden: A novel method integrating graph and transformer representations for predicting the effect of mutations on clinical drug response.Semantic proximity Read

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations.Semantic proximity Read

Enhancing mutation impact prediction in protein-protein interactions through interpretable graph-based multi-level feature interactions.Semantic proximity Read

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.Semantic proximity Read

USP-ddG: a unified structural paradigm with data efficacy and mixture-of-experts for predicting mutational effects on protein-protein interactions.Semantic proximity Read