基于深度神经网络的药物蛋白虚拟筛选

药物的研发是一种投入成本高、耗费时间长且成功率较低的一种研究,为了在药物开发阶段可以快速获得潜在的化合物,针对性地提出一种基于深度神经网络的药物蛋白虚拟筛选的方法。首先从给定数据集中学习如何提取相关特征,获取配体原子和残基类型进行特征分析,快速识别活性分子和非活性分子,然后使用降维方式和K折验证等方法对药物筛选的模型进行处理,最后通过分析富集因子和AUC值验证诱饵化合物与分子蛋白的互相作用验证模型的可靠程度,实验结果表明所提出的筛选方法具有很好的可行性和有效性,有效地加快了虚拟筛选过程。

Virtual Screening of Drug Protein Based on Deep Neural Network

Drug development is a kind of research with high input cost, long development cycle and low success rate. In order to quickly obtain potential compounds in the drug development stage, the paper proposes a deep neural network based virtual screening method for drug proteins. First, by learning how to extract the features from a given data set, the ligand atoms and the residue type are acquired to conduct characteristic analysis. After fast identification of active and inactive molecules, the dimension reduction method and the K-fold validation method are used to process the drug screening model. Finally, by analyzing enrichment factors and the interaction between AUC value bait compounds and molecular protein, the reliability of the model is verified. The experiment proves the feasibility and effectiveness of the proposed screening method which can effectively speed up the virtual screening process.