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Han, Z.* ; Xia, Z. ; Xia, J.* ; Tetko, I.V. ; Wu, S.*

The state-of-the-art machine learning model for plasma protein binding prediction: Computational modeling with OCHEM and experimental validation.

Eur. J. Pharm. Sci. 204:106946 (2025)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Plasma protein binding (PPB) is closely related to pharmacokinetics, pharmacodynamics and drug toxicity. Existing models for predicting PPB often suffer from low prediction accuracy and poor interpretability, especially for high PPB compounds, and are most often not experimentally validated. Here, we carried out a strict data curation protocol, and applied consensus modeling to obtain a model with a coefficient of determination of 0.90 and 0.91 on the training set and the test set, respectively. This model (available on the OCHEM platform https://ochem.eu/article/29) was further retrospectively validated for a set of 63 poly-fluorinated molecules and prospectively validated for a set of 25 highly diverse compounds, and its performance for both these sets was superior to that of the other previously reported models. Furthermore, we identified the physicochemical and structural characteristics of high and low PPB molecules for further structural optimization. Finally, we provide practical and detailed recommendations for structural optimization to decrease PPB binding of lead compounds.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Ochem ; Machine Learning ; Plasma Protein Binding ; Prospective Study ; Retrospective Study; In-silico; Applicability Domain; Chemicals; Toxicity
ISSN (print) / ISBN 0928-0987
e-ISSN 0928-0987
Zeitschrift European Journal of Pharmaceutical Sciences
Quellenangaben Band: 204, Heft: , Seiten: , Artikelnummer: 106946 Supplement: ,
Verlag Elsevier
Verlagsort Radarweg 29, 1043 Nx Amsterdam, Netherlands
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)
Förderungen Program for Foreign Talent of Ministry of Science and Tech-nology of the People's Republic of China
National Science and Technology Major Projects for Major New Drugs Innovation and Development
Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences