A Multi-Task Learning Approach for Data Imputation of Compound Bioactivity Values for the SLC Transporter Superfamily

Solute carrier (SLC) transporters constitute the largest family of membrane transport proteins in humans. They facilitate the movement of ions, neurotransmitters, nutrients, and drugs. Given their critical role in regulating cellular physiology, they are important therapeutic targets for neurological and psychological disorders, metabolic diseases, and cancer. Despite their pharmaceutical relevance, many SLC transporters remain understudied. To address data sparsity of available compound-bioactivity values for SLC transporters, we employed a multi-task learning approach for data imputation of the missing measurements. By leveraging relationships between related tasks, deep learning has previously shown promise in imputing compound bioactivities across multiple assays. We developed a multi-task deep neural network (MTDNN) to impute missing pChEMBL values across the SLC transporter superfamily. With a data matrix density of 2.53% and an R² of 0.74, our model demonstrated robust predictive performance. Specifically, we imputed missing values for 9,122 unique compounds across 54 SLC targets spanning various folds and subfamilies, generating 480,133 predictions from 12,455 known interactions. The advantages of the multi-task learning (MTL) approach were indicated in the ability of certain targets to leverage the shared representation of knowledge and acquire increased predictive accuracy over single-task learning (STL) counterparts. Despite the limitations set by low data density, activity cliffs, and inter-protein heterogeneity, the MTDNN showed promising potential as a data imputation tool within the SLC superfamily.