School of Computer, Guangdong University of Education, Guangzhou 510310, China.
School of Pharmaceutical Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan 528458, China.
Molecules. 2024 Oct 12;29(20):4829. doi: 10.3390/molecules29204829.
This study introduces a deep learning framework based on SMILES representations of chemical structures to predict drug-drug interactions (DDIs). The model extracts Morgan fingerprints and key molecular descriptors, transforming them into raw graphical features for input into a modified ResNet18 architecture. The deep residual network, enhanced with regularization techniques, efficiently addresses training issues such as gradient vanishing and exploding, resulting in superior predictive performance. Experimental results show that StructNet-DDI achieved an AUC of 99.7%, an accuracy of 94.4%, and an AUPR of 99.9%, demonstrating the model's effectiveness and reliability. These findings highlight that StructNet-DDI can effectively extract crucial features from molecular structures, offering a simple yet robust tool for DDI prediction.
本研究提出了一个基于化学结构 SMILES 表示的深度学习框架，用于预测药物-药物相互作用（DDI）。该模型提取了 Morgan 指纹和关键分子描述符，并将其转换为原始图形特征，输入到修改后的 ResNet18 架构中。带有正则化技术的深度残差网络有效地解决了梯度消失和爆炸等训练问题，从而实现了优异的预测性能。实验结果表明，StructNet-DDI 的 AUC 达到 99.7%，准确率为 94.4%，AUPR 为 99.9%，证明了该模型的有效性和可靠性。这些发现表明，StructNet-DDI 可以有效地从分子结构中提取关键特征，为 DDI 预测提供了一种简单而强大的工具。
