Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation

The pervasive and rapidly spreading issue of antimicrobial resistance (AMR) has rendered many traditional antibiotics ineffective, necessitating the urgent discovery and development of new therapeutic compounds. Conventional methods for antibiotic discovery are inherently time-consuming, resource-intensive, and prone to high failure rates, thereby highlighting the imperative for more efficient and innovative strategies. Artificial intelligence (AI) presents a disruptive technological paradigm capable of significantly accelerating and optimizing critical stages within the antibiotic discovery pipeline. This includes advanced target detection, efficient virtual screening of vast chemical libraries, de novo design of novel molecular entities, and enhanced early-stage compound evaluation. This work provides an in-depth examination of how various AI methodologies, including machine learning,

deep learning, natural language processing, and generative models, can be leveraged for the discovery of both small-molecule antibiotics and antimicrobial peptides (AMPs). Specific applications explored encompass sophisticated virtual screening techniques, optimization of pharmacokinetic profiles, predictive modeling for antimicrobial resistance mechanisms, and the rational design of AMPs, supported by relevant case studies. The synergistic integration of AI with emerging fields such as synthetic biology and nanotechnology is also considered, offering pathways for comprehensive antibiotic development.

Message: Hi FE, I need help with “Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation” in “Biotechnology & Biomedical Engineering”