Protein Spatial Structure Meets Artificial Intelligence: Revolutionizing Drug Synergy-Antagonism in Precision...

Area of Science:

• Computational drug discovery and pharmaceutical research.
• Integrative bioinformatics and structural biology.

Background:

• Protein site druggability is crucial for targeted drug design.
• Understanding drug synergy and antagonism is essential for effective therapies.
• Artificial intelligence (AI) offers new avenues for predicting these interactions.

Purpose of the Study:

• To systematically review AI-driven methods for predicting drug synergy and antagonism using protein 3D structures.
• To explore the molecular mechanisms underlying drug interactions.
• To evaluate the application of machine learning and deep learning in drug development.

Main Methods:

• Integration of protein 3D structural data with AI algorithms.
• Analysis of molecular mechanisms (transcription factors, signaling pathways, membrane transport).
• Evaluation of AI model performance using multi-source biological data.

Main Results:

• AI, particularly machine learning and deep learning, shows significant progress in predicting drug synergy-antagonism.
• Methods for identifying drug binding sites and optimizing molecular docking are advancing.
• Understanding multi-target drug mechanisms and structural characteristics is improving.

Conclusions:

• AI and structural biology provide a strong foundation for precision medicine and personalized treatment strategies.
• These advancements facilitate the rational design of novel multi-target drugs.
• Significant clinical and translational implications for cancer, infectious, and metabolic diseases are highlighted.