MixingDTA: improved drug-target affinity prediction by extending mixup with guilt-by-association.

ABSTRACT:

Summary

Drug-target affinity (DTA) prediction is an important regression task for drug discovery, which can provide richer information than traditional drug-target interaction prediction as a binary prediction task. To achieve accurate DTA prediction, quite large amount of data are required for each drug, which is not available as of now. Thus, data scarcity and sparsity is a major challenge. Another important task is "cold-start" DTA prediction for unseen drug or protein. In this work, we introduce MixingDTA, a novel framework to tackle data scarcity by incorporating domain-specific pretrained language models for molecules and proteins with our MEETA (MolFormer and ESM-based Efficient aggregation Transformer for Affinity) model. We further address the label sparsity and cold-start

SUBMITTER: Kim Y 

PROVIDER: S-EPMC12261493 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature