Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties

Hamza Zahid; Hilal Tayara; Kil To Chong

doi:10.1007/s00204-024-03756-9

Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties

Arch Toxicol. 2024 Apr 15. doi: 10.1007/s00204-024-03756-9. Online ahead of print.

Authors

Hamza Zahid¹, Hilal Tayara², Kil To Chong^{3

4}

Affiliations

¹ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
² School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea. hilaltayara@jbnu.ac.kr.
³ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea. kitchong@jbnu.ac.kr.
⁴ Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea. kitchong@jbnu.ac.kr.

PMID: 38619593
DOI: 10.1007/s00204-024-03756-9

Abstract

Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively.

Keywords: Artificial intelligence; Cytochrome P450; Drug discovery; Drug-drug interaction; Inhibition prediction; MACCS key fingerprint; Machine learning; Morgan fingerprints; RDKit fingerprints; SMILES.

Abstract

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