Classification of FLT3 inhibitors and SAR analysis by machine learning methods

Abstract

FMS-like tyrosine kinase 3 (FLT3) is a type III receptor tyrosine kinase, which is an important target for anticancer therapy. In this work, we conducted a structure-activity relationship (SAR) study on 3867 FLT3 inhibitors we collected. MACCS ngerprints, ECFP4 ngerprints, and TT ngerprints were used to represent the inhibitors in the dataset. A total of 36 classi cation models were built based on support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and deep neural networks (DNN) algorithms. Model 3D_3 built by deep neural networks (DNN) and TT ngerprints performed best on the test set with the highest prediction accuracy of 85.83% and Matthews correlation coe cient (MCC) of 0.72 and also performed well on the external test set.In addition, we clustered 3867 inhibitors into 11 subsets by K-Means algorithm to gure out the structural characteristics of the reported FLT3 inhibitors. Finally, we analyzed the SAR of FLT3 inhibitors by RF algorithm based on ECFP4 ngerprints. The results showed that 2-aminopyrimidine, 1-ethylpiperidine, 2,4bis(methylamino)pyrimidine, amino-aromatic heterocycle, [(2E)-but-2-enyl]dimethylamine, but-2-enyl, and alkynyl were typical fragments among highly active inhibitors. Besides, three scaffolds in Subset_A (Subset 4), Subset_B, and Subset_C showed a signi cant relationship to inhibition activity targeting FLT3.

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

“…Janssen et al [19] introduced the Drug Discovery Maps (DDM) model, employing algorithms like t-SNE to visualize and predict interactions across the kinase family, leading to the discovery of potent FLT3 inhibitors. Furthermore, Zhao et al [20] applied ML methods to classify and analyze the structure-activity relationship of a vast number of FLT3 inhibitors, uncovering key structural features associated with high inhibitory activity. These advancements, as discussed by Eckardt et al [21], highlight the growing importance of ML in managing AML, from diagnosis to therapy optimization.…”