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Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R
Donghui Huo1, Hongzhao Wang1, Zijian Qin1
1State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, University of Chemical Technology, Beijing, People's Republic of China.
Abstract:
Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.
Insights
This study developed structure-activity relationship models for small-molecule inhibitors targeting epidermal growth factor receptor (EGFR), including difficult-to-treat double mutations. The models accurately predict inhibitor activity, aiding in the design of novel anticancer drugs.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Drug Discovery
Background:
- Epidermal growth factor receptor (EGFR) is a key target for anticancer drug development.
- Mutations in EGFR, particularly the T790M/L858R double mutation, present significant challenges in cancer treatment.
Purpose of the Study:
- To build and validate structure-activity relationship (SAR) models for small-molecule inhibitors against wild-type and T790M/L858R double-mutant EGFR.
- To identify key structural features associated with high activity against both wild-type and mutant EGFR.
Main Methods:
- Utilized a dataset of 379 compounds to develop 2D classification models (Support Vector Machine, Random Forest, Self-Attention Recurrent Neural Network) using ECFP4 fingerprints and SMILES.
- Employed Self-Organizing Maps for inhibitor clustering and 3D Comparative Molecular Similarity Analysis (3D-CoMSIA) on selected scaffolds.
- Investigated the influence of steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor properties on inhibitor activity.
Main Results:
- All six developed models achieved high accuracy (>0.87) and Matthews correlation coefficient (>0.76) on the test set.
- Identified specific substructures (e.g., anilinoquinoline, methoxy/fluoro phenyl, anilinopyrimidine, acrylamide) associated with potent inhibition of wild-type and mutant EGFR.
- 3D-CoMSIA models demonstrated predictive power (q² > 0.65) for selected scaffolds.
Conclusions:
- The developed SAR models provide a robust framework for predicting the activity of EGFR inhibitors.
- Specific chemical scaffolds and substructures are crucial for targeting both wild-type and mutant EGFR.
- This research facilitates the rational design of more effective anticancer therapeutics against EGFR-mutated cancers.
