A multi-task learning approach for prediction of missing bioactivity values of compounds for the SLC transporter superfamily
Abstract
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.
Fields of Study & Subjects
Machine Learning in Bioinformatics
Computational Drug Discovery Methods
Amino Acid Enzymes and Metabolism
Publication details
- Journal / source
- Digital Discovery
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 5
- Issue
- 2
- Pages
- 878–890
- Publication date
- 1 January 2026
- Language
- EN