[Applications of artificial intelligence to new drug development]

Area of Science:

• Biotechnology
• Computational Biology
• Pharmacology

Background:

• Artificial intelligence (AI) integrates sensing, thinking, acting, and learning.
• AI leverages massive data integration and probabilistic modeling for decision-making.
• Technological advancements enable AI to process complex biological and chemical data.

Purpose of the Study:

• To explore AI's role in drug discovery and development.
• To highlight AI's capability in generating disease models and identifying therapeutic targets.
• To discuss AI's contribution to precision medicine and personalized therapies.

Main Methods:

• AI-driven integration of big multimodal patient data.
• Generation of probabilistic models for disease pathophysiology.
• Computational analysis for drug identification, optimization, and repurposing.

Main Results:

• AI models represent patient heterogeneity and identify causally linked therapeutic targets.
• AI aids in discovering and optimizing drugs and repurposing existing ones.
• AI supports biomarker identification, combination therapy selection, and innovative clinical trial design.

Conclusions:

• The convergence of AI, biotechnology, and drug sciences is driving computational precision medicine.
• AI facilitates the development of therapies precisely tailored to individual patient profiles.
• AI-powered approaches promise to yield novel preventive measures and treatments.