Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks

Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks

Inhibiting the mammalian Target of Rapamycin (mTOR) represents a promising strategy in cancer therapy due to its crucial role in cell growth, survival, and metabolism. Using a variety of quantitative structure-activity relationship (QSAR) models, we present a comprehensive comparison of deep learnin...

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Bibliographic Details
Main Authors: Seyed Alireza Khanghahi, Hadi Kamkar, Seyedehsamaneh Shojaeilangari, Abdollah Allahverdi, Parviz Abdolmaleki
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Computer Methods and Programs in Biomedicine Update
Subjects:
Mammalian target of rapamycin (mTOR)
Quantitative structure-activity relationship (QSAR)
Machine learning
Deep learning
Morgan fingerprints
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Online Access:http://www.sciencedirect.com/science/article/pii/S2666990025000333
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