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

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 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

SUBMITTER: Alcazar JJ 

PROVIDER: S-EPMC11768337 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature