Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer─Design, Synthesis, and In Vitro Evaluation

Hossam Nada, Anam Rana Gul, Ahmed Elkamhawy, Sungdo Kim, Minkyoung Kim, Yongseok Choi, Tae Jung Park, Kyeong Lee

Research output: Contribution to journalArticlepeer-review


The epidermal growth factor receptor (EGFR) is vital for regulating cellular functions, including cell division, migration, survival, apoptosis, angiogenesis, and cancer. EGFR overexpression is an ideal target for anticancer drug development as it is absent from normal tissues, marking it as tumor-specific. Unfortunately, the development of medication resistance limits the therapeutic efficacy of the currently approved EGFR inhibitors, indicating the need for further development. Herein, a machine learning-based application that predicts the bioactivity of novel EGFR inhibitors is presented. Clustering of the EGFR small-molecule inhibitor (∼9000 compounds) library showed that N-substituted quinazolin-4-amine-based compounds made up the largest cluster of EGFR inhibitors (∼2500 compounds). Taking advantage of this finding, rational drug design was used to design a novel series of 4-anilinoquinazoline-based EGFR inhibitors, which were first tested by the developed artificial intelligence application, and only the compounds which were predicted to be active were then chosen to be synthesized. This led to the synthesis of 18 novel compounds, which were subsequently evaluated for cytotoxicity and EGFR inhibitory activity. Among the tested compounds, compound 9 demonstrated the most potent antiproliferative activity, with 2.50 and 1.96 μM activity over MCF-7 and MDA-MB-231 cancer cell lines, respectively. Moreover, compound 9 displayed an EGFR inhibitory activity of 2.53 nM and promising apoptotic results, marking it a potential candidate for breast cancer therapy.

Original languageEnglish
Pages (from-to)31784-31800
Number of pages17
JournalACS Omega
Issue number35
Publication statusPublished - 2023 Sept 5

Bibliographical note

Funding Information:
This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [no. 2018R1A5A2023127] and [no. 2023R1A2C3004599]. This work is also supported by the BK21 FOUR program, which was funded by the Ministry of Education of Korea through NRF.

Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society.

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering


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