Wang Ying, Su Yangguang, Zhao Kairui, Huo Diwei, Du Zhenshun, Wang Zhiju, Xie Hongbo, Liu Lei, Jin Qing, Ren Xuekun, Chen Xiujie, Zhang Denan
Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
The Fourth Hospital of Harbin Medical University, No.37 Yiyuan Street, Harbin, Heilongjiang, 150001, China.
Heliyon. 2024 Jul 14;10(14):e34244. doi: 10.1016/j.heliyon.2024.e34244. eCollection 2024 Jul 30.
At the beginning of the "Disease X" outbreak, drug discovery and development are often challenged by insufficient and unbalanced data. To address this problem and maximize the information value of limited data, we propose a drug screening model, LGCNN, based on convolutional neural network (CNN), which enables rapid drug screening by integrating features of drug molecular structures and drug-target interactions at both local and global (LG) levels. Experimental results show that LGCNN exhibits better performance compared to other state-of-the-art classification methods under limited data. In addition, LGCNN was applied to anti-SARS-CoV-2 drug screening to realize therapeutic drug mining against COVID-19. LGCNN transcends the limitations of traditional models for predicting interactions between single drug targets and shows new advantages in predicting multi-target drug-target interactions. Notably, the cross-coronavirus generalizability of the model is also implied by the analysis of targets, drugs, and mechanisms in the prediction results. In conclusion, LGCNN provides new ideas and methods for rapid drug screening in emergency situations where data are scarce.
在“疾病X”爆发之初，药物研发往往受到数据不足和不均衡的挑战。为解决这一问题并最大化有限数据的信息价值，我们提出了一种基于卷积神经网络（CNN）的药物筛选模型LGCNN，该模型通过整合药物分子结构和药物-靶点相互作用在局部和全局（LG）层面的特征，实现快速药物筛选。实验结果表明，在数据有限的情况下，LGCNN与其他现有先进分类方法相比表现更优。此外，LGCNN被应用于抗SARS-CoV-2药物筛选，以实现针对COVID-19的治疗性药物挖掘。LGCNN超越了传统模型预测单一药物靶点相互作用的局限性，在预测多靶点药物-靶点相互作用方面展现出新优势。值得注意的是，通过对预测结果中的靶点、药物和作用机制进行分析，还暗示了该模型对不同冠状病毒的通用性。总之，LGCNN为数据稀缺的紧急情况下的快速药物筛选提供了新的思路和方法。
