Development of Drug Discovery Platforms Using Artificial Intelligence and Cheminformatics

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery

Background:

• Artificial intelligence (AI) has advanced significantly, impacting various scientific fields.
• Machine learning models are increasingly applied in pharmaceutical research.
• AI offers diverse tools for drug discovery, from data analysis to predictive modeling.

Purpose of the Study:

• To review the application of AI in small-molecule drug discovery.
• To highlight AI's role in molecular design, activity prediction, and binding pose prediction.
• To discuss AI's utility in academic drug discovery settings.

Main Methods:

• Literature review of AI applications in drug discovery.
• Focus on machine learning techniques for small-molecule design and prediction.
• Analysis of AI's contribution to understanding compound-target interactions.

Main Results:

• AI models demonstrate significant progress in designing novel small molecules.
• Machine learning accurately predicts the biological activity of potential drug compounds.
• AI effectively predicts the binding poses of compounds to target proteins.

Conclusions:

• AI, particularly machine learning, is a powerful tool revolutionizing small-molecule drug discovery.
• AI applications streamline molecular design, activity, and binding predictions.
• The integration of AI in academic research accelerates the drug discovery pipeline.