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

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.

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

“…To address the problem of resistance, researchers are exploring various computer-assisted approaches for drug design. These methods include quantitative structure–activity relationship (QSAR) 10 – 12 , machine learning (ML)-based models 13 – 15 , deep learning (DL)-based models 16 , molecular docking 10 , 17 , 18 , molecular dynamic simulations 18 , 19 , and pharmacophore analysis 18 , among others. It's important to note that most of these research endeavors primarily focus on targeting ERα rather than ERβ 20 .…”