MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors

Rizki Triyani Pusparini, Adila Alfa Krisnadhi, Firdayani

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study presents a framework for analyzing the quantitative structure–activity relationship (QSAR) of estrogen receptor alpha inhibitors. Our approach utilizes supervised learning, integrating self-attention Transformer and molecular graph information, to predict estrogen receptor alpha inhibitors. We established five classification models for predicting these inhibitors in breast cancer. Among these models, our proposed MATH model achieved remarkable precision, recall, F1 score, and specificity, with values of 0.952, 0.972, 0.960, and 0.922, respectively, alongside an ROC AUC of 0.977. MATH exhibited robust performance, suggesting its potential to assist pharmaceutical and health researchers in identifying candidate compounds for estrogen alpha inhibitors and guiding drug discovery pathways.

Original languageEnglish
Article number5843
JournalMolecules
Volume28
Issue number15
DOIs
Publication statusPublished - Aug 2023

Keywords

  • artificial intelligence
  • breast cancer
  • estrogen receptor alpha
  • molecular graph structure
  • QSAR
  • Transformer

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