Artificial Intelligence Tools for Drug Target Discovery Research: Database, Tools, Applications, and Challenges.

Area of Science:

• Biomedical research
• Computational biology
• Drug discovery

Background:

• Drug target identification is a major challenge in pharmaceutical research.
• Artificial intelligence (AI) offers powerful tools for predicting drug-target interactions.
• AI can analyze large biomedical datasets to understand drug mechanisms.

Purpose of the Study:

• To provide a comprehensive overview of AI applications in drug target discovery.
• To highlight the potential and challenges of AI in pharmaceutical research.
• To offer practical guidance for integrating AI into drug discovery workflows.

Main Methods:

• Review of recent public databases and computational methods.
• Analysis of AI-driven approaches for drug-target interaction prediction.
• Exploration of user-friendly AI tools for researchers.

Main Results:

• AI significantly enhances the efficiency and accuracy of virtual screening, binding affinity estimation, and target identification.
• AI enables deeper insights into complex biological networks and drug mechanisms.
• Key challenges include ensuring prediction precision and integrating AI into existing workflows.

Conclusions:

• AI holds immense potential to accelerate the discovery of novel therapeutic drugs.
• Overcoming integration barriers and ensuring prediction accuracy are crucial for widespread AI adoption.
• This review empowers researchers, even those without computational expertise, to leverage AI for drug discovery.