Artificial intelligence in drug development: reshaping the therapeutic landscape.

Area of Science:

• Pharmaceutical Sciences
• Computational Biology
• Drug Discovery

Background:

• Artificial intelligence (AI) has significantly advanced over the last 30 years.
• Machine learning, deep learning, and neural networks are key AI technologies impacting drug development.
• AI is increasingly used in pharmaceutical research and development (R&D).

Purpose of the Study:

• To explore the transformative impact of AI on medication research and development.
• To highlight AI's role in drug design, target identification, and clinical trial predictions.
• To discuss the potential and challenges of AI in bringing new medicines to market.

Main Methods:

• Review of AI applications in identifying therapeutic targets.
• Analysis of AI's role in improving chemical designs and predicting protein structures.
• Examination of generative AI's contribution to medicinal molecule development.

Main Results:

• AI has accelerated R&D by identifying new targets, improving designs, and predicting protein structures.
• Generative AI is speeding up the development of molecules for various diseases.
• AI-driven discovery has reduced development times and costs, despite challenges with model transparency and bias.

Conclusions:

• AI is a powerful tool revolutionizing drug discovery and development.
• Despite current limitations, AI is poised to make drug discovery faster, more efficient, and widespread.
• AI offers new treatment options and is expected to drive future pharmaceutical innovation.