Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity

Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity

Dimensionality reduction techniques are crucial for enabling deep learning driven quantitative structure-activity relationship (QSAR) models to navigate higher dimensional toxicological spaces, however the use of specific techniques is often arbitrary and poorly explored. Six dimensionality techniqu...

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Bibliographic Details
Main Authors: Alexander D. Kalian, Emilio Benfenati, Olivia J. Osborne, David Gott, Claire Potter, Jean-Lou C. M. Dorne, Miao Guo, Christer Hogstrand
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Toxics
Subjects:
QSAR
dimensionality reduction
deep learning
autoencoder
principal component analysis
locally linear embedding
Online Access:https://www.mdpi.com/2305-6304/11/7/572

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https://www.mdpi.com/2305-6304/11/7/572

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