Top-DTI: integrating topological deep learning and large language models for drug-target interaction prediction.

ABSTRACT:

Motivation

The accurate prediction of drug-target interactions (DTI) is a crucial step in drug discovery, providing a foundation for identifying novel therapeutics. Traditional drug development is both costly and time-consuming, often spanning over a decade. Computational approaches help narrow the pool of compound candidates, offering significant starting points for experimental validation. In this study, we propose a Top-DTI framework for predicting DTI by integrating topological data analysis (TDA) with large language models (LLMs). Top-DTI leverages persistent homology to extract topological features from protein contact maps and drug molecular images. Simultaneously, protein and drug LLMs generate semantically rich embeddings that capture sequential and contextual information

SUBMITTER: Talo M 

PROVIDER: S-EPMC12261471 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature