Target identification and assessment in the era of AI

Area of Science:

• Biotechnology
• Computational Biology
• Pharmacology

Background:

• Drug discovery is a lengthy, costly, and high-risk process.
• Traditional target identification and validation are time-consuming, with validation often occurring post-regulatory approval.
• Identifying effective drug targets is critical for successful drug development.

Purpose of the Study:

• To review recent advancements in drug target discovery and selection.
• To highlight the role and impact of artificial intelligence (AI) in therapeutic target exploration.
• To discuss challenges and limitations of AI in drug discovery.

Main Methods:

• Review of recent literature on AI applications in drug target identification.
• Analysis of key considerations in selecting therapeutic targets.
• Examination of AI-driven approaches for target assessment and validation.

Main Results:

• AI significantly enhances the analysis of large datasets and biological networks for target identification.
• AI-driven methods are accelerating the exploration of novel therapeutic targets.
• Several AI-identified targets have progressed to drug candidates in clinical trials.

Conclusions:

• AI is revolutionizing drug target identification and validation, increasing efficiency and success probability.
• AI tools offer powerful solutions to overcome traditional bottlenecks in drug discovery.
• Continued development and application of AI are crucial for future therapeutic advancements.