Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization

Area of Science:

• Pharmaceutical R&D
• Computational Chemistry
• Bioinformatics

Background:

• Drug discovery is a complex and costly process in pharmaceutical research and development.
• Artificial intelligence (AI) offers powerful tools to enhance various stages of drug discovery and development.
• AI facilitates molecular feature extraction, drug-target interaction analysis, and disease-relationship modeling.

Purpose of the Study:

• To review recent advancements in AI applications for drug design.
• To provide insights into AI-driven strategies for target identification, synthetic accessibility, lead optimization, and ADMET property evaluation.
• To establish a conceptual framework for implementing AI in pharmaceutical research.

Main Methods:

• Review of current literature on AI applications in drug discovery.
• Summarization of deep learning tools and methodologies.
• Organization of AI-driven strategies for different drug discovery phases.

Main Results:

• AI improves prediction accuracy, accelerates timelines, and reduces costs in drug discovery.
• AI applications cover target identification, synthetic accessibility, lead optimization, and ADMET evaluation.
• Deep learning tools are increasingly important for guiding AI implementation.

Conclusions:

• AI significantly enhances efficiency and success rates in pharmaceutical R&D.
• A structured approach to AI implementation is crucial for advancing drug discovery.
• This review provides a framework for leveraging AI methodologies in pharmaceutical research.