Deep Learning-Driven Protein-Ligand Binding Affinity Prediction: Data, Architecture, Training and Evaluation.

Area of Science:

• Computational Biology
• Drug Discovery
• Machine Learning

Background:

• Protein-ligand binding affinity (PLA) prediction is vital for drug discovery.
• Deep learning (DL) models provide a computationally efficient alternative to experimental assays and traditional scoring functions.
• A knowledge gap hinders the effective integration of biological and computational insights in DL model design for PLA.

Purpose of the Study:

• To provide a comprehensive guide for training DL models for PLA prediction.
• To explore key considerations including datasets, data processing, model architecture, training strategies, and evaluation.
• To discuss applications and challenges of DL in PLA prediction for drug discovery.

Main Methods:

• Review of current literature on DL for PLA prediction.
• Analysis of critical factors in DL model development: data, interpretability, biological plausibility.
• Exploration of training strategies and evaluation methodologies.

Main Results:

• DL models show promise for rapid and scalable PLA prediction.
• Challenges include data heterogeneity, model interpretability, and ensuring biological relevance.
• Effective DL model training requires careful consideration of multiple interconnected factors.

Conclusions:

• DL offers a powerful approach to accelerate drug discovery through accurate PLA prediction.
• Bridging the gap between computational biology and DL is essential for optimal model development.
• Further research is needed to overcome current challenges and fully leverage DL in this field.