Tokopedia-UI AI Center of Excellence, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia.
Research Center for Vaccine and Drugs, Research Organization for Health, National Research and Innovation Agency (BRIN), Jakarta 10340, Indonesia.
Molecules. 2023 Aug 3;28(15):5843. doi: 10.3390/molecules28155843.
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.
乳腺癌是女性死亡的第二大主要原因，但早期筛查和自我意识可以帮助预防它。针对雌激素水平的激素治疗药物提供了潜在的治疗方法。然而，传统的药物发现需要广泛而昂贵的过程。本研究提出了一种分析雌激素受体α抑制剂的定量构效关系（QSAR）的框架。我们的方法利用有监督学习，整合自注意力转换器和分子图信息，来预测雌激素受体α抑制剂。我们建立了五个用于预测乳腺癌中这些抑制剂的分类模型。在这些模型中，我们提出的 MATH 模型在预测雌激素受体α抑制剂方面表现出了显著的精度、召回率、F1 得分和特异性，分别为 0.952、0.972、0.960 和 0.922，同时 ROC AUC 为 0.977。MATH 表现出稳健的性能，表明它有可能帮助制药和健康研究人员识别候选化合物作为雌激素α抑制剂，并指导药物发现途径。
