Antibiotic resistance is a major global health threat that requires the discovery of new and effective antibiotics. However, the traditional methods of antibiotic discovery are slow, costly, and inefficient. This minireview presents the discovery of a new class of antibiotics using AI, which is a powerful and innovative tool for accelerating and enhancing the drug discovery process. A web-based search was used to extract data. The new antibiotics were identified by a deep learning model that can learn from the graph representation of chemical structures, and that can also provide explainable and interpretable predictions of antibiotic potency. The new antibiotics have novel and diverse chemical structures and mechanisms of action, which are unlike any existing class of antibiotics. The new antibiotics have shown promising activity against a wide range of drug-resistant bacteria, both in vitro and in vivo and have a low propensity to induce resistance. The discovery of the new antibiotics demonstrates the potential of AI in antibiotic discovery, and also opens up new avenues and opportunities for further research and innovation in this field. However, there are also several challenges and limitations that need to be overcome before the new antibiotics can be translated into clinical use, such as ensuring their safety and efficacy in humans, optimizing their pharmacokinetics and pharmacodynamics, and scaling up their production and distribution. The article stresses on crucial issues by highlighting the importance of ethical principles, guidelines, and regulations that can ensure the responsible and ethical use of AI in medicine.
Key words: Artificial intelligence. Antibiotic discovery, Antibiotic resistance, Deep learning, Drug development, Novel antibiotics, Mechanism of action, Pharmacokinetics, Ethical issues, Clinical trials
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