Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Protein Language Model-Based Fitness Estimates Facilitate Resistance Mutation Identification.

Journal of chemical information and modeling - 27 Jul 2026

Schwarz Dominik, Giese Sven H, Gupta Akansha, Dziubańska-Kusibab Paulina J, Villalba Santiago D, Cherniack Andrew, Yang Xiaoping, Root David, Karsli-Uzunbas Gizem, Greulich Heidi, Siegel Franziska, Kamburov Atanas, Nevedomskaya Ekaterina, Christ Clara, Aldeghi Matteo, Mortier Jérémie

Abstract excerpt

Drug resistance is a major challenge in cancer therapy. Cancer cells with pre-existing or acquired mutations that confer resistance to a given drug treatment outgrow the susceptible cell population and cause cancer recurrence after an initial successful treatment response. Knowledge about resistance mutations before they occur in the clinic could prevent unnecessary patient treatment with ineffective drugs, in...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Drug Resistance, Neoplasm
Share to ErbB Receptors

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mitogen-Activated Protein Kinase 1

Share to Models, Molecular

Share to Mutation

Share to Protein Kinase Inhibitors

Share to Thermodynamics

Source

Identifiers

PMID: 42439482
DOI: 10.1021/acs.jcim.6c00768

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Protein Language Model-Based Fitness Estimates Facilitate Resistance Mutation Identification.PMID 42439482Read ↗

Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven ApproachesSemantic proximity Read ↗

Improvement in predicting drug sensitivity changes associated with protein mutations using a molecular dynamics based alchemical mutation method.Semantic proximity Read ↗

Revealing acquired resistance mechanisms of kinase-targeted drugs using an on-the-fly, function-site interaction fingerprint approachSemantic proximity Read ↗

PremPLI: Predicting the Effects of Missense Mutations on Protein-Ligand InteractionsSemantic proximity Read ↗

MechPPI: Binding Mechanism-based Machine-Learning tool for Predicting Protein-Protein Binding Affinity Changes Upon MutationsSemantic proximity Read ↗

Prioritizing stability-enhancing mutations using the ESM protein language model in conjunction with physics-based MM/GBSA predictions.Semantic proximity Read ↗

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects.Semantic proximity Read ↗

Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures.Semantic proximity Read ↗

Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures.Semantic proximity Read ↗

Personalized prediction of EGFR mutation-induced drug resistance in lung cancer.Semantic proximity Read ↗

Combining Mutational Signatures, Clonal Fitness, and Drug Affinity to Define Drug-Specific Resistance Mutations in Cancer.Semantic proximity Read ↗

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinaseSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.