Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1

Zhou, Jiajun; Wu, Shiying; Lee, Boon Giin; Chen, Tianwei; He, Ziqi; Lei, Yukun; Tang, Bencan; Hirst, Jonathan D.

Abstract

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules.

Citation

Zhou, J., Wu, S., Lee, B. G., Chen, T., He, Z., Lei, Y., …Hirst, J. D. (2021). Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1. Molecules, 26(24), Article 7492. https://doi.org/10.3390/molecules26247492

• Journal Article Type: Article
• Acceptance Date: Dec 6, 2021
• Online Publication Date: Dec 10, 2021
• Publication Date: Dec 2, 2021
• Deposit Date: Jan 5, 2022
• Publicly Available Date: Jan 6, 2022
• Journal: Molecules
• Electronic ISSN: 1420-3049
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 24
• Article Number: 7492
• DOI: https://doi.org/10.3390/molecules26247492
• Keywords: Chemistry (miscellaneous); Analytical Chemistry; Organic Chemistry; Physical and Theoretical Chemistry; Molecular Medicine; Drug Discovery; Pharmaceutical Science
• Public URL: https://nottingham-repository.worktribe.com/output/7108932
• Publisher URL: https://www.mdpi.com/1420-3049/26/24/7492