ArticleACS central science2019
Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches.
Article in ACS central science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 55 citations in OpenAlex.
- Rapid, accurate, and reproduciblemSphere · 2025Article
- Multiple Molecule λ-Dynamics: Probing Drug Resistance with Concurrent Protein and Ligand Perturbations.The journal of physical chemistry letters · 2025Article
- A Thermodynamic Cycle to Predict the Competitive Inhibition Outcomes of an Evolving Enzyme.Journal of chemical theory and computation · 2025Article
- Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding.The journal of physical chemistry. B · 2025Article
- Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding.bioRxiv : the preprint server for biology · 2025Article
- Evaluating data partitioning strategies for accurate prediction of protein-ligand binding free energy changes in mutated proteins.Computational and structural biotechnology journal · 2025Article
- Article
- Prediction of Amino Acid Substitutions in ABL1 Protein Leading to Tumor Drug Resistance Based on "Structure-Property" Relationship Classification Models.Life (Basel, Switzerland) · 2023Article
- Evaluating the ability of end-point methods to predict the binding affinity tendency of protein kinase inhibitors.RSC advances · 2023Article
- Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex.Journal of chemical theory and computation · 2023Article
- Elucidating Residue-Level Determinants Affecting Dimerization of Ebola Virus Matrix Protein Using High-Throughput Site Saturation Mutagenesis and Biophysical Approaches.The journal of physical chemistry. B · 2023Article
- Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex.bioRxiv : the preprint server for biology · 2023Article
- A mutation-induced drug resistance database (MdrDB).Communications chemistry · 2023Article
- Predicting antibiotic resistance in complex protein targets using alchemical free energy methods.Journal of computational chemistry · 2022Article
- Predicting the mutation effects of protein-ligand interactions via end-point binding free energy calculations: strategies and analyses.Journal of cheminformatics · 2022Article
- Covalent docking and molecular dynamics simulations reveal the specificity-shifting mutations Ala237Arg and Ala237Lys in TEM beta-lactamase.PLoS computational biology · 2022Article
- PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.Communications biology · 2021Article
- Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches.Communications chemistry · 2021Article
- FGFR-TKI resistance in cancer: current status and perspectives.Journal of hematology & oncology · 2021Review
- How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations?ACS omega · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Resistance to small molecule drugs often emerges in cancer cells, viruses, and bacteria as a result of the evolutionary pressure exerted by the therapy. Protein mutations that directly impair drug binding are frequently involved in resistance, and the ability to anticipate these mutations would be beneficial in drug development and clinical practice. Here, we evaluate the ability of three distinct computational methods to predict ligand binding affinity changes upon protein mutation for the cancer target Abl kinase. These structure-based approaches rely on first-principle statistical mechanics, mixed physics- and knowledge-based potentials, and machine learning, and were able to estimate binding affinity changes and identify resistant mutations with remarkable accuracy. We expect that these complementary approaches will enable the routine prediction of resistance-causing mutations in a variety of other target proteins.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.