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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Pharmaceuticals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8247/18/1/96 |
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| Summary: | <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 relationship (QSAR) model to predict the inhibitory potency (pIC<sub>50</sub> values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. <b>Methods:</b> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi mathvariant="normal">Q</mi><mn>2</mn></msup></semantics></math></inline-formula> values of 0.926 and 0.922, respectively, while external validation on 270 independent compounds resulted in an R<sup>2</sup> value of 0.941 with a standard deviation of 0.237. <b>Results:</b> 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 pIC<sub>50</sub> values and facilitate ligand-based virtual screening, leading to the identification of promising FLT3 inhibitors. <b>Conclusions:</b> 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. |
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| ISSN: | 1424-8247 |