一种融合胶囊网络的分类方法

目前的ADMET分类方法在对具有多特征性和特征关联性的化合物数据进行ADMET分类时存在不足。而且，对ADMET分类结果不具备可解释性。针对上述问题，提出一种融合胶囊网络的分类模型（CapsMC）。CapsMC模型首先提出一种feature-to-image图像转换算法。使用该算法将特征之间的关联关系和依赖关系作为考量纳入到分类依据中，实现特征的多层次提取。其次，探索胶囊网络的高级应用，提出一种认知推理机制。使用该机制对特征进行认知推理，实现ADMET的可解释性分类。模型在五种ADMET数据集上的实验结果表明，CapsMC模型可以高效实现ADMET的可解释性分类。

Classification model based on capsule network

The current ADMET classification methods have shortcomings in classifying the ADMET of compounds data with multiple characteristics and feature correlation. Moreover, the classification results of ADMET are not explainable. To solve these problems, this paper proposed a classification model based on capsule network(CapsMC). CapsMC model first proposed the Feature-to-Image algorithm. It used this algorithm to consider the correlation and dependence between features into the classification basis, and realized the multi-level extraction of features. Second, it explored the advanced application of capsule network, and designed a cognitive reasoning mechanism. It used this mechanism to carry out cognitive reasoning on features, and realized explainable classification of ADMET. Experimental results on five ADMET datasets show that CapsMC can achieve the explainable classification of ADMET well.