A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation
This is an incremental review paper for researchers and practitioners in pharmaceuticals and AI, summarizing existing methods without introducing new techniques.
The paper tackles the slow and costly traditional drug discovery process by providing an overview of how AI and ML tools can streamline and accelerate it, including for antibiotics to address antimicrobial resistance, though no specific results or numbers are reported.
Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here, we provide a comprehensive overview of different AI and ML tools that can be used to streamline and accelerate the drug discovery process. By using data sets to train ML algorithms, it is possible to discover drugs or drug-like compounds relatively quickly, and efficiently. Additionally, we address limitations in AI-based drug discovery and development, including the scarcity of high-quality data to train AI models and ethical considerations. The growing impact of AI on the pharmaceutical industry is also highlighted. Finally, we discuss how AI and ML can expedite the discovery of new antibiotics to combat the problem of worldwide antimicrobial resistance (AMR).