Artificial Intelligence in Drug Discovery: Integrative Advances From Data to Therapeutic Innovation

Area of Science:

• Pharmaceutical Research
• Computational Chemistry
• Biotechnology

Background:

• Traditional drug discovery is costly, time-consuming, and has low success rates.
• AI technologies like machine learning (ML), deep learning (DL), and reinforcement learning (RL) offer solutions.

Purpose of the Study:

• To review the impact of AI across the entire drug discovery pipeline.
• To highlight AI's role in accelerating the development of novel therapeutics.

Main Methods:

• AI enhances target identification, virtual screening, and de novo drug design.
• AI improves toxicity prediction and pharmacokinetic/pharmacodynamic profiling.
• AI optimizes chemical synthesis via retrosynthesis prediction and reaction yield modeling.

Main Results:

• AI significantly increases efficiency in preclinical and clinical phases.
• AI improves patient stratification, biomarker discovery, and adaptive trial designs.
• AI facilitates personalized medicine through polypharmacology and drug repurposing.

Conclusions:

• AI integration revolutionizes pharmaceutical research, expediting the delivery of life-saving medications.
• Future directions include addressing AI limitations like data quality, interpretability, and regulatory acceptance.