ArticleScientific reports2022
Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Monte Carlo-based QSPR modeling of drop-weight impact sensitivity of energetic materials using hybrid SMILES-graph descriptors.RSC advances · 2026Article
- Machine Learning-Driven Discovery of Novel HER2 Inhibitors Through Integrated Virtual Screening and Molecular Dynamics Simulations.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Decoding GuaB: Machine Learning-Powered Discovery of Enzyme Inhibitors Against the SuperbugPharmaceuticals (Basel, Switzerland) · 2025Article
- Using the Coefficient of Conformism of a Correlative Prediction in Simulation of Cardiotoxicity.Toxics · 2025Article
- Construction of reliable QSPR models for predicting the impact sensitivity of nitroenergetic compounds using correlation weights of the fragments of molecular structures.Scientific reports · 2025Article
- Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm.PloS one · 2025Article
- Models for the No-Observed-Effect Concentration (NOEC) and Maximal Half-Effective Concentration (EC50).Toxics · 2024Article
- Genetic descriptor search algorithm for predicting hydrogen adsorption free energy of 2D material.Scientific reports · 2023Article
- In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization.BMC chemistry · 2023Article
- QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLBMC chemistry · 2023Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R
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