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A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase

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dc.contributor.authorAlcázar, Jackson
dc.contributor.authorSánchez, Ignacio
dc.contributor.authorMerino, Cristian
dc.contributor.authorMonasterio, Bruno
dc.contributor.authorSajuria, Gaspar
dc.contributor.authorMiranda, Diego
dc.contributor.authorDíaz, Felipe
dc.contributor.authorCampodónico, Paola
dc.date.accessioned2025-03-06T13:11:14Z
dc.date.available2025-03-06T13:11:14Z
dc.date.issued2025
dc.description.abstractBackground/Objectives: Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC50 values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Methods: Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting. Rigorous internal validation via leave-one-out and 10-fold cross-validation yielded Q2 values of 0.926 and 0.922, respectively, while external validation on 270 independent compounds resulted in an R2 value of 0.941 with a standard deviation of 0.237. Results: Key molecular descriptors influencing the inhibitor potency were identified, thereby improving the interpretability of structural requirements. Additionally, a user-friendly computational tool was developed to enable rapid prediction of pIC50 values and facilitate ligand-based virtual screening, leading to the identification of promising FLT3 inhibitors. Conclusions: These results represent a significant advancement in the field of FLT3 inhibitor discovery, offering a reliable, practical, and efficient approach for early-stage drug development, potentially accelerating the creation of targeted therapies for AML.
dc.description.versionVersión Publicada
dc.identifier.citationAlcázar JJ, Sánchez I, Merino C, Monasterio B, Sajuria G, Miranda D, Díaz F, Campodónico PR. A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase. Pharmaceuticals (Basel). 2025 Jan 14;18(1):96. doi: 10.3390/ph18010096.
dc.identifier.doihttps://doi.org/10.3390/ph18010096
dc.identifier.urihttps://hdl.handle.net/11447/9899
dc.language.isoen
dc.subjectAML treatment
dc.subjectFLT3 inhibitors
dc.subjectQSAR modeling
dc.subjectComputer-aided drug design
dc.subjectLigand-based drug design
dc.titleA Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourcePharmaceuticals
dspace.entity.typePublication

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