Recent Advances in Deep Learning-Based Drug-Target Binding Affinity Prediction

2026-08-13

AI Summary

This study addresses the limitations in accuracy and evaluation of drug-target binding affinity prediction by systematically reviewing deep learning models and benchmark datasets. It identifies critical bottlenecks, including data bias, poor cold-start generalization, and the absence of standardized evaluation metrics. To overcome these challenges, this work proposes novel research paradigms encompassing standardized evaluation frameworks, improved dataset curation, and multimodal representation integration. By elucidating the core deficiencies of current methodologies, this paper establishes a future research roadmap prioritizing robustness and generalizability. Ultimately, this work provides essential theoretical foundations and methodological guidance to advance precision drug discovery, ensuring more reliable computational predictions in pharmaceutical development.

Abstract

Computational approaches to drug discovery involve multiple sub-problems, and among them, drug-target binding affinity prediction plays an important role. Despite recent advances, accurately predicting binding affinity remains an open research area. The major objective of our paper is to perform a comprehensive review and comparative analysis of recent machine learning methods for drug-target binding affinity prediction, with a focus on identifying strengths, limitations, and research gaps. We review representative recent deep learning approaches that use common benchmark datasets and evaluation metrics, covering a range of neural network architectures and representation strategies. In addition, we analyze seven widely used benchmark datasets and commonly adopted evaluation metrics for drug-target binding affinity prediction. Our analysis indicates that although many methods report strong performance on standard benchmarks, their effectiveness is often influenced by dataset bias and limited evaluation settings. Furthermore, most methods exhibit reduced performance in cold-start scenarios, highlighting challenges in generalization. We identify several limitations of current approaches, including dataset imbalance, the lack of standardized evaluation, limited real-world applicability, and challenges in cold-start scenarios. We also discuss future research directions, including better dataset design, more robust evaluation methods, improved handling of cold-start problems, and the integration of multimodal representations.

Problem

• Drug-Target Binding Affinity Prediction
• Deep Learning
• Cold-start Problem
• Dataset Bias
• Evaluation Metrics

Innovation

• Drug-Target Binding Affinity Prediction
• Deep Learning
• Benchmark Analysis
• Cold-start Scenario
• Multimodal Representation