mTORC2型
PI3K/AKT/mTOR通路
mTORC1型
计算生物学
自噬
激酶
人工智能
随机森林
雷帕霉素的作用靶点
机器学习
计算机科学
生物
信号转导
细胞生物学
生物化学
细胞凋亡
作者
Chetna Kumari,Muhammad Abulaish,Naidu Subbarao
标识
DOI:10.1109/tcbb.2020.2964203
摘要
Mammalian Target of Rapamycin (mTOR) is a Ser/Thr protein kinase, and its role is integral to the autophagy pathway in cancer. Targeting mTOR for therapeutic interventions in cancer through autophagy pathway is challenging due to the dual roles of autophagy in tumor progression. The architecture of mTOR reveals two complexes - mTORC1 and mTORC2, each having multiple protein subunits. mTOR kinase inhibitors target the structurally and functionally similar catalytic subunits of both mTORC1 and mTORC2. In this paper, we have explored two different categories of molecular features - descriptors and fingerprints for developing predictive models using machine learning techniques. Random Forest variable importance measures and autoencoders are used to identify molecular descriptors and fingerprints, respectively. We have built various predictive models using identified features and their combination for predicting mTOR kinase inhibitors. Finally, the best model based on the Mathew correlation co-efficient value over the validation dataset is selected for screening kinase SARfari bioactivity dataset. In this study, we have identified twenty best performing descriptors for predicting mTOR kinase inhibitors. To the best of our knowledge, it is the first study on integrating traditional machine learning and deep learning-based approaches for feature extraction to predict mTOR kinase inhibitors.
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