Abstract Multi-target modeling can be used for inhibition prediction of CA isoforms, the essential zinc metalloenzymes involved in different biological processes such as tumorigenesis. In this study, the first multi-target model is developed for predicting the activities of inhibitors against CA-I, CA-II, CA-IX, and CA-XII. Structural similarity analysis is carried out for two cancer-related isoforms CA-IX and CA-XII. The mean TM-score value (0.935) reveals a marked similarity between the two structures. To select relevant descriptors for the developed multi-target model, we propose a novel feature selection method based on shared subspace learning, which considers correlation among different targets in multi-target modeling. The proposed shared subspace feature selection method uses the mixed convex and non-convex l2,p-norm (0 ​

中文翻译：

---

通过利用多个目标之间的共享信息，在碳酸酐酶同种型的多目标建模中进行稀疏特征选择

摘要 多靶点建模可用于 CA 亚型的抑制预测，CA 亚型是参与不同生物过程（如肿瘤发生）的必需锌金属酶。在这项研究中，开发了第一个多目标模型，用于预测抑制剂对 CA-I、CA-II、CA-IX 和 CA-XII 的活性。对两种癌症相关的同种型 CA-IX 和 CA-XII 进行了结构相似性分析。平均 TM 分数值 (0.935) 揭示了两种结构之间的显着相似性。为了为开发的多目标模型选择相关描述符，我们提出了一种基于共享子空间学习的新特征选择方法，该方法考虑了多目标建模中不同目标之间的相关性。提出的共享子空间特征选择方法使用混合凸非凸l2,p-norm(0