Artificial Intelligence in Antibiotic Discovery

Abstract

Antimicrobial resistance (AMR) is increasing rapidly, making many old antibiotics less effective. Because traditional methods of discovering new antibiotics are slow, expensive, and often unsuccessful, new approaches are needed. Artificial intelligence (AI) is emerging as a powerful tool that can speed up and improve the process of antibiotic discovery. This review explains how AI techniques like machine learning, deep learning, natural language processing, and generative models are used to discover new antibiotics and antimicrobial peptides (AMPs). AI helps in important steps such as identifying drug targets, screening compounds, designing new molecules, improving drug properties, and predicting resistance.

The article also discusses how AI can work together with fields like synthetic biology and nanotechnology to develop advanced treatments, including personalized therapies. However, challenges such as limited data, bias in algorithms, and difficulties in applying research to clinical practice still exist.

Overall, combining AI with laboratory research offers a promising and efficient way to develop new antibiotics and fight antimicrobial resistance.

Keywords: Artificial intelligence; Antimicrobial resistance; Antibiotic discovery; Machine learning; Deep learning; Generative models; Antimicrobial peptides