Artificial Intelligence in Computer-Aided Drug Design (CADD) Tools for the Finding of Potent Biologically Active...

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery and development

Background:

• Computer-Aided Drug Design (CADD) is a long-established field, but computational approaches are gaining significant traction in academia and industry.
• The increasing availability of molecular data, 3D structures, and binding information fuels the acceptance of AI in drug discovery.
• Artificial intelligence (AI), bioinformatics, and data science are key drivers accelerating treatment development, reducing costs, and minimizing animal testing.

Purpose of the Study:

• To review recent advancements in AI-driven drug discovery and development (DDD).
• To examine the potential of AI to reshape the entire DDD process.
• To discuss the challenges and opportunities presented by AI in identifying potent, target-specific small molecule ligands.

Main Methods:

• Review of current literature on AI applications in drug discovery.
• Analysis of AI's role in optimizing pharmacodynamic, pharmacokinetic, and clinical properties.
• Exploration of AI-driven ligand screening using virtual libraries and deep learning predictions.

Main Results:

• AI is becoming a fundamental component in drug discovery, particularly for small molecules.
• AI expedites the identification of diverse, potent, target-specific, and drug-like ligands.
• AI-driven approaches enhance the efficiency and cost-effectiveness of developing novel therapeutics.

Conclusions:

• AI significantly reshapes the drug discovery landscape, offering faster and more economical routes to new medicines.
• AI facilitates the development of safer and more effective therapeutics by optimizing molecular properties and target interactions.
• Continued advancements in AI promise to further revolutionize the pharmaceutical industry and patient outcomes.