Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review

TL;DR

This review paper discusses recent advances in deep learning and graph neural network models for drug-target interaction prediction, highlighting their potential to improve drug discovery efficiency and accuracy.

Contribution

It provides a comprehensive analysis of 180 methods from 2016 to 2025, detailing their architectures, input representations, and innovations in the field.

Findings

Deep learning models outperform traditional methods in accuracy.

Graph neural networks effectively capture complex drug-target relationships.

The review identifies promising research directions for future development.

Abstract

Drug discovery remains a slow and expensive process that involves many steps, from detecting the target structure to obtaining approval from the Food and Drug Administration (FDA), and is often riddled with safety concerns. Accurate prediction of how drugs interact with their targets and the development of new drugs by using better methods and technologies have immense potential to speed up this process, ultimately leading to faster delivery of life-saving medications. Traditional methods used for drug-target interaction prediction show limitations, particularly in capturing complex relationships between drugs and their targets. As an outcome, deep learning models have been presented to overcome the challenges of interaction prediction through their precise and efficient end results. By outlining promising research avenues and models, each with a different solution but similar to the problem, this paper aims to give researchers a better idea of methods for even more accurate and efficient prediction of drug-target interaction, ultimately accelerating the development of more effective drugs. A total of 180 prediction methods for drug-target interactions were analyzed throughout the period spanning 2016 to 2025 using different frameworks based on machine learning, mainly deep learning and graph neural networks. Additionally, this paper discusses the novelty, architecture, and input representation of these models.