Document Type

Article

Publication Date

2025

DOI

10.1093/bib/bbaf491

Publication Title

Briefing in Bioinformatics

Volume

26

Issue

5

Pages

bbaf491 (1-24)

Abstract

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug discovery research since researchers started using deep learning as a potent tool for DTB prediction. In particular, we examine how methodologies have evolved, starting with early heterogeneous network-based approaches, progressing to graph-based approaches that were widely accepted, followed by modern attention-based architectures, and finally, the most recent multimodal approaches. We also provide case studies utilizing an extensive compound library against specific protein targets implicated in critical cancer pathways to demonstrate the usefulness of these approaches. In addition to summarizing the latest developments in DTB prediction models, this review also identifies their drawbacks. It also highlights the outlook for the DTB prediction domain and future research directions. Combined, these studies present a more comprehensive view of how deep learning offers a quantitative framework for researching drug-target relationships, speeding up the identification of new drug candidates and making it easier to identify possible DTBs.

Rights

© 2025 The Authors.

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Data Availability

Article states: "No new data were generated or analyzed in support of this research."

Original Publication Citation

Debnath, K., Rana, P., & Ghosh, P. (2025). A survey on deep learning for drug-target binding prediction: Models, benchmarks, evaluation, and case studies. Briefings in Bioinformatics, 26(5), 1-24, Article bbaf491. https://doi.org/10.1093/bib/bbaf491

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