基于注意力的多输入深度学习生物活性预测架构:在EGFR抑制剂中的应用

Huy Pham, Trung Le
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引用次数: 4

摘要

近几十年来,机器学习和深度学习在药物发现领域得到了广泛的应用,并取得了巨大的成功。从历史上看,机器学习和深度学习模型是通过分离模型对结构数据或化学性质进行训练的。在本研究中,我们提出了一种同时训练两种类型数据的架构,以提高整体性能。给出了以SMILES符号表示的分子结构及其标签,生成了基于SMILES的特征矩阵和分子描述符。这些数据在深度学习模型上进行训练,该模型还集成了注意力机制,以方便训练和解释。实验表明,该模型的预测性能比参考模型有所提高。通过对EGFR抑制剂数据集的交叉验证,我们的架构的最大MCC为0.58,AUC为90%,优于参考模型。我们还成功地将注意力机制整合到我们的模型中,这有助于解释化学结构对生物活性的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors
Machine learning and deep learning have gained popularity and achieved immense success in Drug discovery in recent decades. Historically, machine learning and deep learning models were trained on either structural data or chemical properties by separated model. In this study, we proposed an architecture training simultaneously both type of data in order to improve the overall performance. Given the molecular structure in the form of SMILES notation and their label, we generated the SMILES-based feature matrix and molecular descriptors. These data were trained on a deep learning model which was also integrated with the Attention mechanism to facilitate training and interpreting. Experiments showed that our model could raise the performance of prediction comparing to the reference. With the maximum MCC 0.58 and AUC 90% by cross-validation on EGFR inhibitors dataset, our architecture was outperforming the referring model. We also successfully integrated Attention mechanism into our model, which helped to interpret the contribution of chemical structures on bioactivity.
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