Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration

Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration

In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines aut...

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Bibliographic Details
Main Authors: Keerthi Krishnan, Ryan Kassab, Steve Agajanian, Gennady Verkhivker
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:International Journal of Molecular Sciences
Subjects:
autonomous molecular design
deep learning models
latent space landscapes
protein kinases
kinase inhibitors
perturbation chemical modeling
Online Access:https://www.mdpi.com/1422-0067/23/19/11262

Internet

https://www.mdpi.com/1422-0067/23/19/11262

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