Work

A multi-task learning approach for prediction of missing bioactivity values of compounds for the SLC transporter superfamily

Year: 2026

Type: article

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.

Source: Digital Discovery

DOI: 10.1039/d5dd00536a

Authors: Tarik Ćerimagić, Sergey Sosnin, Gerhard F. Ecker

Institution: University of Vienna

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W7119506503

FWCI: 3.564

Cites: 35

Cited by: 1

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