Using Jupyter Notebooks for re-training machine learning models
- Author(s)
- Aljosa Smajic, Melanie Grandits, Gerhard F. Ecker
- 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.
- Organisation(s)
- Department of Pharmaceutical Sciences
- Journal
- Journal of Cheminformatics
- Volume
- 14
- No. of pages
- 9
- ISSN
- 1758-2946
- DOI
- https://doi.org/10.1186/s13321-022-00635-2
- Publication date
- 08-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 301207 Pharmaceutical chemistry
- Keywords
- ASJC Scopus subject areas
- Library and Information Sciences, Computer Science Applications, Physical and Theoretical Chemistry, Computer Graphics and Computer-Aided Design
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/using-jupyter-notebooks-for-retraining-machine-learning-models(07a1579d-7acd-44b8-abed-43b566a482f0).html