Discovery of novel EGFR and BRAF inhibitors by machine learning approach

Hailong Zhang; Henry H.Y. Tong

doi:10.1016/j.intonc.2024.10.001

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Discovery of novel EGFR and BRAF inhibitors by machine learning approach

Research article | 更新时间：2026-06-03

- Discovery of novel EGFR and BRAF inhibitors by machine learning approach
- Discovery of novel EGFR and BRAF inhibitors by machine learning approach
- 智能肿瘤学 2025年1卷第1期页码：7-16
- 作者机构：
  
  1. Faculty of Applied Sciences, Macao Polytechnic University, Macao Special Administrative Region,China,999078
  2. Faculty of Health Sciences and Sports, Macao Polytechnic University, Macao Special Administrative Region,China,999078
- 作者简介：
- 基金信息：
- DOI：10.1016/j.intonc.2024.10.001
  中图分类号：
- 收稿：2024-03-30，
  
  网络首发：2024-12-20，
  
  纸质出版：2025
- 稿件说明：
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Hailong Zhang, Henry H.Y. Tong. Discovery of novel EGFR and BRAF inhibitors by machine learning approach[J]. 智能肿瘤学, 2025,1(1):7-16.

Hailong Zhang, Henry H.Y. Tong. Discovery of novel EGFR and BRAF inhibitors by machine learning approach[J]. Intelligent Oncology, 2025, 1(1): 7-16.
Hailong Zhang, Henry H.Y. Tong. Discovery of novel EGFR and BRAF inhibitors by machine learning approach[J]. 智能肿瘤学, 2025,1(1):7-16. DOI： 10.1016/j.intonc.2024.10.001.

Hailong Zhang, Henry H.Y. Tong. Discovery of novel EGFR and BRAF inhibitors by machine learning approach[J]. Intelligent Oncology, 2025, 1(1): 7-16. DOI： 10.1016/j.intonc.2024.10.001.

摘要

BackgroundIn the pharmaceutical industry

epidermal growth factor receptor (EGFR) and B-Raf proto-oncogene

serine/threonine kinase (BRAF) are the primary targets for targeted cancer therapy.ObjectiveThis study aimed to develop novel models for predicting the bioactivity of EGFR and BRAF inhibitors and to uncover critical substructural groups for EGFR and BRAF targets.MethodK-nearest neighbor

Gaussian naive Bayes

random forest (RF)

decision tree

and support vector machine models were utilized to develop classifiers for EGFR and BRAF inhibitors. In addition

the critical functional groups of these inhibitors were analyzed

and molecular docking was performed.ResultsThe RF model was found to be the preferred choice for predicting the bioactivity of EGFR and BRAF inhibitors. The RF model for BRAF had an accuracy and area under the curve (AUC) of 93% and 0.96

respectively

and the RF model for EGFR had an accuracy and AUC of 90% and 0.96

respectively. The results of this study show the importance of properly increasing chlorine or fluorine in inhibitors of BRAF or EGFR. Moreover

the EGFR and BRAF inhibitors exhibited the same halogen selection priority of fluorine chlorine bromine.ConclusionThe present study develops novel models for predicting the bioactivity of EGFR and BRAF inhibitors. The hydrogen bond formation is a key factor for influencing the selection priority of fluorine chlorine bromine.

Abstract

BackgroundIn the pharmaceutical industry

epidermal growth factor receptor (EGFR) and B-Raf proto-oncogene

Gaussian naive Bayes

random forest (RF)

decision tree

and support vector machine models were utilized to develop classifiers for EGFR and BRAF inhibitors. In addition

the critical functional groups of these inhibitors were analyzed

respectively

and the RF model for EGFR had an accuracy and AUC of 90% and 0.96

respectively. The results of this study show the importance of properly increasing chlorine or fluorine in inhibitors of BRAF or EGFR. Moreover

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