Zonyfar Candra, Ngnamsie Njimbouom Soualihou, Mosalla Sophia, Kim Jeong-Dong
Department of Computer Science and Electronic Engineering, Sun Moon University, Asan, 31460, Republic of Korea.
Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea.
J Cheminform. 2024 Oct 29;16(1):119. doi: 10.1186/s13321-024-00915-z.
State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors. This model significantly enhances the discrimination between inhibitors and non-inhibitors for five major CYP450 isozymes: 1A2, 2C9, 2C19, 2D6, and 3A4. GTransCYPs learns information patterns from molecular graphs by aggregating node and edge representations using a transformer. The GTransCYPs model utilizes transformer convolution layers to process features, followed by a global attention-pooling technique to synthesize the graph-level information. This information is then fed through successive linear layers for final output generation. Experimental results demonstrate that the GTransCYPs model achieved high performance, outperforming other state-of-the-art methods in CYP450 prediction.Scientific contributionThe prediction of CYP450 inhibition via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we presented a deep learning (DL) architecture based on GNN with transformer mechanism and attention pooling (GTransCYPs) to predict CYP450 inhibitors. Four GTransCYPs of different pooling technique were tested on an experimental tasks on the CYP450 prediction problem for the first time. Graph transformer with attention pooling algorithm achieved the best performances. Comparative and ablation experiments provide evidence of the efficacy of our proposed method in predicting CYP450 inhibitors. The source code is publicly available at https://github.com/zonwoo/GTransCYPs .
最新的医学研究证明，在药物研发的早期阶段预测CYP450酶抑制剂是有益的。然而，基于机器学习的准确计算机模拟方法来预测CYP450抑制剂仍然具有挑战性。在此，我们介绍了GTransCYPs，一种改进的具有Transformer机制的图神经网络（GNN），用于预测CYP450抑制剂。该模型显著提高了对五种主要CYP450同工酶（1A2、2C9、2C19、2D6和3A4）抑制剂与非抑制剂之间的区分能力。GTransCYPs通过使用Transformer聚合节点和边的表示来从分子图中学习信息模式。GTransCYPs模型利用Transformer卷积层处理特征，随后采用全局注意力池化技术来合成图级信息。然后，这些信息通过连续的线性层进行最终输出生成。实验结果表明，GTransCYPs模型具有高性能，在CYP450预测方面优于其他现有方法。
科学贡献
利用生物信息通过计算技术预测CYP450抑制作用已成为一种经济高效的方法。在此，我们提出了一种基于具有Transformer机制和注意力池化的GNN的深度学习（DL）架构（GTransCYPs）来预测CYP450抑制剂。首次在CYP450预测问题的实验任务上测试了四种不同池化技术的GTransCYPs。具有注意力池化算法的图Transformer取得了最佳性能。对比和消融实验证明了我们提出的方法在预测CYP450抑制剂方面的有效性。源代码可在https://github.com/zonwoo/GTransCYPs上公开获取。
