A multi-task learning approach for prediction of missing bioactivity values of compounds for the SLC transporter superfamily
A multi-task deep neural network uses compound descriptors to predict missing bioactivity values for multiple targets. The model learns shared representations to complete the sparse pChEMBL (pC) matrix across the SLC transporter superfamily.
Authors
- Sergey Sosnin
- Tarik Cerimagic
- Gerhard F. Ecker
Institutions
- University of Vienna (AT)
Publication Details
- Journal
- Digital Discovery
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1039/d5dd00536a
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00