Has AI Reshaped Drug Discovery, or Is There Still a Long Way to Go?

Area of Science:

• Pharmaceutical Sciences
• Computational Biology
• Drug Discovery

Background:

• Traditional drug discovery is lengthy, expensive, and inefficient.
• Artificial intelligence (AI) adoption surged in the 2010s due to computational and data advancements.
• AI offers potential solutions to streamline the drug development pipeline.

Purpose of the Study:

• To evaluate the impact and limitations of AI in modern drug discovery.
• To identify key challenges hindering the full regulatory approval of AI-discovered drugs.
• To assess AI's role as a supportive tool versus a standalone solution.

Main Methods:

• Review of AI applications in drug discovery stages: target identification, hit discovery, lead optimization.
• Analysis of AI techniques: molecular property prediction, protein structure modeling, NLP, ADME/Tox prediction.
• Examination of clinical trial progress and regulatory approval status of AI-guided molecules.

Main Results:

• AI has enhanced efficiency and reduced costs in early drug discovery.
• Several AI-guided molecules show promising early-phase clinical trial success.
• No drug solely originated by AI has yet received full regulatory approval.

Conclusions:

• AI significantly accelerates early-stage drug discovery but faces limitations.
• Challenges include data quality, model interpretability, and biological complexity.
• Human expertise and experimental validation remain crucial for successful drug development.