Εξειδίκευση τύπου :	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing
Δημιουργός/Συγγραφέας:	Ouzounis, Sotirios Panagiotopoulos, Vasilis Bafiti, Vivi [EL] Ζουμπουλάκης, Παναγιώτης[EN] Zoumpoulakis, Panagiotis Cavouras, Dionisis Kalatzis, Ioannis Matsoukas, Minos-Timotheos [EL] Κατσίλα, Θεοδώρα[EN] Katsila, Theodora
Ημερομηνία:	2023-07
Γλώσσα:	Αγγλικά
ISSN:	1557-8100
DOI:	10.1089/omi.2023.0075
Άλλο:	37406257
Περίληψη:	Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.
Τίτλος πηγής δημοσίευσης:	Omics : a journal of integrative biology
Τόμος/Κεφάλαιο:	27
Τεύχος:	7
Θεματική Κατηγορία:	[EL] Δομική Βιολογία[EN] Structural Biology [EL] Βιοπληροφορική[EN] Bioinformatics [EL] Φαρμακευτική χημεία[EN] Pharmaceutical chemistry [EL] Χημική Βιολογία[EN] Chemical Biology
Λέξεις-Κλειδιά:	ADME-Tox Cytochrome P450 Drug repurposing Machine learning Predictive computational models Quantitative structure-activity relationships
EU Grant:	Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE
EU Grant identifier:	T2EDK-03153
Κάτοχος πνευματικών δικαιωμάτων:	© 2023 Sotiris Ouzounis, et al.
Όροι και προϋποθέσεις δικαιωμάτων:	This Open Access article is distributed under the terms of the Creative Commons License [CC-BY] (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Ηλεκτρονική διεύθυνση στον εκδότη (link):	https://www.liebertpub.com/doi/10.1089/omi.2023.0075
Εμφανίζεται στις συλλογές:	Ινστιτούτο Χημικής Βιολογίας - Επιστημονικό έργο