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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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A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase.pdf (3.85 MB)

Date

2025

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Article

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Artículos Medicina y Ciencias de la Salud

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100

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2695
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https://doi.org/10.3390/ph18010096

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https://hdl.handle.net/11447/9899

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Abstract

Background/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 ro...

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Citation

Alcázar 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.

Keywords

AML treatment , FLT3 inhibitors , QSAR modeling , Computer-aided drug design , Ligand-based drug design

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