Prediction of drug-target binding affinity based on deep learning models.

Area of Science:

• Computational chemistry
• Pharmacology
• Artificial intelligence in drug discovery

Background:

• Drug-target binding affinity (DTA) prediction is crucial for efficient drug discovery.
• Traditional virtual screening methods have limitations in improving drug development success rates.
• Deep learning (DL) offers a promising approach to enhance DTA prediction accuracy.

Purpose of the Study:

• To review the current literature on deep learning models for drug-target binding affinity prediction.
• To summarize key aspects including datasets, metrics, and algorithms used in DL-based DTA prediction.
• To discuss the opportunities, challenges, and future prospects of DL in drug discovery.

Main Methods:

• Comprehensive literature review of studies employing deep learning for DTA prediction.
• Analysis of various deep learning frameworks such as CNN, GCN, and RNN.
• Examination of input representations, performance metrics, and model interpretability.

Main Results:

• Deep learning models show significant potential in advancing DTA prediction accuracy.
• A wide range of DL algorithms and architectures are being applied to DTA prediction.
• Standardized datasets and robust evaluation metrics are essential for model comparison.

Conclusions:

• Deep learning represents a paradigm shift in DTA prediction, moving beyond traditional machine learning.
• Addressing challenges in model interpretability and data standardization is key for future progress.
• DL frameworks hold substantial promise for accelerating drug discovery and development.