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Article

Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer

1
Department of Bio & Medical Big Data (BK21 Program), Division of Life Sciences, Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 5282, Korea
2
Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Division of Applied Life Science, Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea
3
Department of Food Science and Technology, Yeungnam University, Gyeongsan 38541, Gyeongsangbuk-do, Korea
4
Division of Applied Life Sciences, Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 5282, Korea
5
Division of Life Science and Applied Life Science (BK 21 Four), College of Natural Sciences, Gyeongsang National University, Jinju 5282, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Osvaldo Andrade Santos-Filho
Pharmaceuticals 2021, 14(7), 699; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14070699
Received: 9 June 2021 / Revised: 14 July 2021 / Accepted: 14 July 2021 / Published: 20 July 2021
(This article belongs to the Section Medicinal Chemistry)
Disruption of epigenetic processes to eradicate tumor cells is among the most promising interventions for cancer control. EZH2 (Enhancer of zeste homolog 2), a catalytic component of polycomb repressive complex 2 (PRC2), methylates lysine 27 of histone H3 to promote transcriptional silencing and is an important drug target for controlling cancer via epigenetic processes. In the present study, we have developed various predictive models for modeling the inhibitory activity of EZH2. Binary and multiclass models were built using SVM, random forest and XGBoost methods. Rigorous validation approaches including predictiveness curve, Y-randomization and applicability domain (AD) were employed for evaluation of the developed models. Eighteen descriptors selected from Boruta methods have been used for modeling. For binary classification, random forest and XGBoost achieved an accuracy of 0.80 and 0.82, respectively, on external test set. Contrastingly, for multiclass models, random forest and XGBoost achieved an accuracy of 0.73 and 0.75, respectively. 500 Y-randomization runs demonstrate that the models were robust and the correlations were not by chance. Evaluation metrics from predictiveness curve show that the selected eighteen descriptors predict active compounds with total gain (TG) of 0.79 and 0.59 for XGBoost and random forest, respectively. Validated models were further used for virtual screening and molecular docking in search of potential hits. A total of 221 compounds were commonly predicted as active with above the set probability threshold and also under the AD of training set. Molecular docking revealed that three compounds have reasonable binding energy and favorable interactions with critical residues in the active site of EZH2. In conclusion, we highlighted the potential of rigorously validated models for accurately predicting and ranking the activities of lead molecules against cancer epigenetic targets. The models presented in this study represent the platform for development of EZH2 inhibitors. View Full-Text
Keywords: cancer; epigenetic; PRC2; machine learning; multi-class models cancer; epigenetic; PRC2; machine learning; multi-class models
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MDPI and ACS Style

Danishuddin; Kumar, V.; Parate, S.; Bahuguna, A.; Lee, G.; Kim, M.O.; Lee, K.W. Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer. Pharmaceuticals 2021, 14, 699. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14070699

AMA Style

Danishuddin, Kumar V, Parate S, Bahuguna A, Lee G, Kim MO, Lee KW. Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer. Pharmaceuticals. 2021; 14(7):699. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14070699

Chicago/Turabian Style

Danishuddin, Vikas Kumar, Shraddha Parate, Ashutosh Bahuguna, Gihwan Lee, Myeong O. Kim, and Keun W. Lee 2021. "Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer" Pharmaceuticals 14, no. 7: 699. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14070699

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