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

2025-02-21

AI Summary

Low prediction accuracy and computational inefficiency of drug–target interaction (DTI) models hinder accelerated drug discovery. Method: We systematically review 180 DTI prediction models published between 2016 and 2025, analyzing architectural evolution—particularly in deep learning and graph neural networks (GNNs)—with emphasis on multimodal molecular representations (e.g., sequence, 3D structure, topological embeddings) and end-to-end trainable paradigms. Contribution/Results: We propose four design principles for high-generalization DTI models, identify state-of-the-art approaches—including GNN-Transformer hybrid architectures—and establish a unified evaluation benchmark. Our work delivers a reproducible technical roadmap, methodological guidelines, and open-source implementation references. Empirical results demonstrate significant improvements in both prediction accuracy and computational efficiency, thereby facilitating safer and faster drug discovery.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Accelerate drug discovery process
• Improve drug-target interaction prediction
• Overcome traditional methods limitations

Innovation

• Deep learning for interaction prediction
• Graph neural networks in drug discovery
• Machine learning frameworks analysis