Titel
Using Jupyter Notebooks for re-training machine learning models
Abstract
Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In addition, privacy concerns often restrict the access to sufficient data, leading to models with a narrow chemical space. Therefore, we propose a framework of re-trainable models that can be transferred from one local instance to another, and further allow a less extensive descriptor selection. The models are shared via a Jupyter Notebook, allowing the evaluation and implementation of a broader chemical space by keeping most of the tunable parameters pre-defined. This enables the models to be updated in a decentralized, facile, and fast manner. Herein, the method was evaluated with six transporter datasets (BCRP, BSEP, OATP1B1, OATP1B3, MRP3, P-gp), which revealed the general applicability of this approach.
Stichwort
Classification modelsTransporter proteinsDecentralizationRe-trainingJupyter Notebook
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:1671672
Erschienen in
Titel
Journal of Cheminformatics
Band
14
Ausgabe
1
ISSN
1758-2946
Erscheinungsdatum
2022
Verlag
Springer Science and Business Media LLC
Erscheinungsdatum
2022
Zugänglichkeit
Rechteangabe
© The Author(s) 2022

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