Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists

Maria Antoniou, Konstantinos D. Papavasileiou, Georgia Melagraki, Francesco Dondero, Iseult Lynch, Antreas Afantitis

Risultato della ricerca: Contributo su rivistaArticolo in rivistapeer review

Abstract

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure–activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform’s graphical user interface (GUI).

Lingua originaleInglese
Numero di articolo5216
RivistaInternational Journal of Molecular Sciences
Volume25
Numero di pubblicazione10
DOI
Stato di pubblicazionePubblicato - mag 2024

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