Alcázar, J.J.; Sánchez, I.; Merino, C.; Monasterio, B.; Sajuria, G.; Miranda, D.; Díaz, F.; Campodónico, P.R. A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase. Preprints2024, 2024031433. https://doi.org/10.20944/preprints202403.1433.v1
APA Style
Alcázar, J.J., Sánchez, I., Merino, C., Monasterio, B., Sajuria, G., Miranda, D., Díaz, F., & Campodónico, P.R. (2024). A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase. Preprints. https://doi.org/10.20944/preprints202403.1433.v1
Chicago/Turabian Style
Alcázar, J.J., Felipe Díaz and Paola R. Campodónico. 2024 "A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase" Preprints. https://doi.org/10.20944/preprints202403.1433.v1
Abstract
In this study, a simple machine learning-based quantitative structure-activity relationship (QSAR) model was developed to predict the inhibitory potency (pIC50 values) of FLT3 tyrosine kinase inhibitors, pivotal in treating Acute Myeloid Leukemia (AML). Distinctively, our model leverages an extensive and diverse dataset, 14 times larger than those employed in prior studies within this field, enabling an unparalleled scope of compound analysis. This vast dataset, combined with further exploration of molecular descriptors, enabled predictions of extraordinary precision, covering a broader spectrum of FLT3 inhibitors than was previously possible. The Random Forest Regressor (RFR) algorithm, selected for its superior predictive performance, was trained with 1080 inputs and validated through comprehensive external and internal methods. It achieved an remarkable coefficient of determination (R^2) of 0.941 and a standard deviation of 0.235 on a test set of 270 compounds, highlighting the efficacy of model in predicting FLT3 inhibitory activity. Key molecular descriptors were identified, enhancing our understanding of structural requirements for inhibitor potency. Additionally, we developed a user-friendly computational tool that enables the rapid prediction of pIC50 values. Utilizing this tool, potential FLT3 inhibitors were identified through ligand-based virtual screening. This study represents a major advancement in FLT3 inhibitor discovery by utilizing a simple QSAR-machine learning model. It enables more efficient and precise identification of potential drug candidates at an early stage, promising a faster development of targeted therapies and streamlining the ligand-based drug design process.
Keywords
FLT3 Inhibitors; Ligand-based Drug Design; Computer-Aided Drug Design; QSAR Modeling; AML Treatment
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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