DrugTar improves druggability prediction by integrating large language models and gene ontologies.

ABSTRACT:

Motivation

Target discovery is crucial in drug development, especially for complex chronic diseases. Recent advances in high-throughput technologies and the explosion of biomedical data have highlighted the potential of computational druggability prediction methods. However, most current methods rely on sequence-based features with machine learning, which often face challenges related to hand-crafted features, reproducibility, and accessibility. Moreover, the potential of raw sequence and protein structure has not been fully investigated.

Results

Here, we leveraged both protein sequence and structure using deep learning techniques, revealing that protein sequence, especially pre-trained embeddings, is more informative than protein structure. Next, we developed DrugTar, a hig

SUBMITTER: Borhani N 

PROVIDER: S-EPMC12312791 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature