Ivanova, M.L., Russo, N., Djaid, N and Nikolic, Konstantin ORCID: https://orcid.org/0000-0002-6551-2977 (2024) Application of machine learning for predicting G9a inhibitors. Digital Discovery.

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Abstract

Object and significance: the G9a enzyme is an epigenomic regulator, making gene expression directly dependent on how various substances in the cell affect this enzyme. Therefore, it is crucial to consider this impact in any biochemical research involving the development of new compounds introduced into the body. While this can be examined experimentally, it would be highly advantageous to predict these effects using computer simulations. Purpose: the purpose of the model was to assist in answering the question of the potential effect that a compound under development could have on the G9a activity, and thus reduce the need for laboratory experiments and facilitate faster and more productive research and development. Solution: the paper proposes a cost-effective machine learning model that determines whether a compound is an active G9a inhibitor. The proposed approach utilises the already existing very extensive PubChem database. The starting point was the quantitative high-throughput screening assay for inhibitors of histone lysine methyltransferase G9a (also available on PubChem) which screened around 350000 compounds. For these compounds, datasets of 60 features were created. Then different ML algorithms were deployed to find the best performing one, which can then be used to predict if some untested compound would actively inhibit G9a. Results: six different ML classifiers have been implemented on five dataset variations. Different variants of the dataset were created by using two different data balancing approaches and including or not the influence of water solubility at a pH of 7.4. The most successful combination was a dataset with five features and a random forest classifier that reached 90% accuracy. The classifier was trained with 60244 and tested with 15062 compounds. Feature reduction was obtained by analysing three different feature importance algorithms, which resulted in not only feature reduction but also some insights for further biochemical research.

Item Type:	Article
Identifier:	10.1039/D4DD00101J
Subjects:	Computing
Depositing User:	Marc Forster
Date Deposited:	24 Sep 2024 08:45
Last Modified:	04 Nov 2024 11:20
URI:	https://repository.uwl.ac.uk/id/eprint/12714

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