Artificial Intelligence in Drug Discovery for Bacterial Infections: Methodologies, Applications, and Future Directions presents a comprehensive discussion of how AI is transforming the process of seeking new antibacterial treatments. As antimicrobial resistance (AMR) climbs to critical worldwide levels of public health concern, conventional drug discovery strategies commonly lag behind rapidly mutating bacterial threats. This book fills that gap by offering state-of-the-art AI-based methods that speed up target identification, automate virtual screening, improve predictive modeling, and facilitate de novo molecular design.
Through straightforward descriptions and detailed chapters, readers are familiarized with the incorporation of omics data into AI models, predictive modeling of resistance strategies, and AI application in drug repurposing, stewardship, and infection control. Case studies serve to emphasize actual applications, while ethical, regulatory, and data considerations discussions prompt critical thinking on the potential as well as the constraints of AI utilization in biomedical research.
Written by microbiologist Saakshi Sharma, the book combines ideas from microbiology, computational biology, pharmacology, and artificial intelligence to offer a genuine interdisciplinary view. It has been crafted for researchers, students, and professionals in the life sciences, biotechnology, medicine, and data science who want to know how AI can revolutionize antibacterial discovery and public health.
By marrying scientific discipline with visionary perception, this book provides its readers with the expertise needed to value present developments and foresee future trends in AI-assisted antibacterial drug discovery—a critical guide in the fight against superbugs and the ongoing worldwide AMR epidemic.
