A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC<sub>50</sub> Inhibition Values of FLT3 Tyrosine Kinase

A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC<sub>50</sub> Inhibition Values of FLT3 Tyrosine Kinase

<b>Background/Objectives:</b> 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 rel...

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Bibliographic Details
Main Authors: Jackson J. Alcázar, Ignacio Sánchez, Cristian Merino, Bruno Monasterio, Gaspar Sajuria, Diego Miranda, Felipe Díaz, Paola R. Campodónico
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Pharmaceuticals
Subjects:
FLT3 inhibitors
ligand-based drug design
computer-aided drug design
QSAR modeling
AML treatment
Online Access:https://www.mdpi.com/1424-8247/18/1/96
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