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Krishnan K, Kassab R, Agajanian S, Verkhivker G. Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration. International Journal of Molecular Sciences. 2022; 23(19):11262. https://doi.org/10.3390/ijms231911262

Krishnan, Keerthi, Ryan Kassab, Steve Agajanian, and Gennady Verkhivker. 2022. "Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration" International Journal of Molecular Sciences 23, no. 19: 11262. https://doi.org/10.3390/ijms231911262

Krishnan, K., Kassab, R., Agajanian, S., & Verkhivker, G. (2022). Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration. International Journal of Molecular Sciences, 23(19), 11262. https://doi.org/10.3390/ijms231911262