Bioinformatics, AI, and Machine Learning in Microbial Drug Development

Bioinformatics, AI, and Machine Learning in Microbial Drug Development by Vagish Dwibedi

Unlock the future of antimicrobial discovery with a practical, research-driven guide that bridges computational science and drug development. This authoritative text brings together cutting-edge bioinformatics, artificial intelligence, and machine learning approaches tailored to microbial targets, making complex methods accessible to scientists, clinicians, and industry professionals.

Begin with a compelling overview of why computational strategies are essential for tackling antimicrobial resistance and accelerating lead identification. Explore real-world workflows: genomic data mining, target prediction, virtual screening, ML-driven optimization, and in silico pharmacokinetics — all explained with clarity and professional insight. Case studies highlight successful applications across bacterial, fungal, and viral pathogens, illustrating practical outcomes for drug discovery teams.

Gain actionable skills and strategic perspective: learn how to integrate omics datasets, choose appropriate algorithms, interpret model outputs, and translate predictions into experimental validation. Whether you’re in academia, biotech, or a pharmaceutical lab, this book equips you to reduce time-to-hit, lower costs, and improve candidate selection.

Globally relevant and regionally useful—from established hubs in North America and Europe to emerging research centers in Asia and Africa—this title supports a worldwide effort against infectious disease. Clear figures, stepwise methodologies, and a professional tone make it ideal for postgraduate students, researchers, and decision-makers.

Ready to transform microbial drug development with data-driven precision? Add Bioinformatics, AI, and Machine Learning in Microbial Drug Development to your library and lead the next wave of antimicrobial innovation.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.