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Exploring Molecular Descriptors and Fingerprints to Predict mTOR Kinase Inhibitors using Machine Learning Techniques
Abstract:
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.
Insights
This study develops predictive models to identify Mammalian Target of Rapamycin (mTOR) kinase inhibitors. By integrating machine learning and deep learning, researchers identified key molecular features for effective drug discovery in cancer therapy.
Area of Science:
- Biochemistry
- Molecular Biology
- Pharmacology
Background:
- Mammalian Target of Rapamycin (mTOR) is a critical kinase in cancer's autophagy pathway.
- Autophagy's dual role in tumor progression complicates therapeutic targeting of mTOR.
- mTOR functions via two complexes, mTORC1 and mTORC2, targeted by kinase inhibitors.
Purpose of the Study:
- To develop predictive models for identifying mTOR kinase inhibitors.
- To explore molecular descriptors and fingerprints for feature extraction.
- To integrate traditional and deep learning approaches for enhanced prediction.
Main Methods:
- Utilized machine learning techniques for predictive model development.
- Employed Random Forest for descriptor importance and autoencoders for fingerprint identification.
- Built and validated models using identified molecular features and their combinations.
Main Results:
- Identified twenty best-performing molecular descriptors for predicting mTOR kinase inhibitors.
- Selected the optimal model based on Mathew correlation coefficient for further screening.
- Successfully integrated traditional and deep learning for feature extraction in mTOR inhibitor prediction.
Conclusions:
- This study presents a novel approach for predicting mTOR kinase inhibitors.
- The findings contribute to understanding molecular features crucial for targeting the mTOR pathway in cancer.
- The integrated methodology offers a powerful tool for accelerating drug discovery for mTOR-related diseases.
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