Machine Learning and Artificial Intelligence in Combating Multidrug-Resistant Bacterial Infections

1. Auflage 2027
ISBN: 978-1-041-12110-7
Verlag: Taylor & Francis

Buch, Englisch, 376 Seiten, Format (B × H): 156 mm x 234 mm

This book explores how artificial intelligence and machine learning can help solve one of healthcare’s most critical challenges: multidrug-resistant bacterial infections. Covering AI/ML techniques, resistance mechanisms, diagnostics, drug discovery, and case studies, it offers a multidisciplinary approach combining microbiology, bioinformatics, and data science. Beginning with foundational AI/ML methods, including supervised and unsupervised learning, deep learning, and neural networks, it explores how these tools can analyse complex biological datasets and identify patterns that traditional approaches often miss. With real-world case studies and discussion of ethical and regulatory issues, it provides a practical and forward-looking guide to leveraging AI/ML for combating MDR pathogens.

The book:

- Covers key algorithms and their relevance to healthcare, including deep learning and neural networks.

- Explains genetic, biochemical, and microbial factors that contribute to antibiotic resistance.

- Shows how machine learning models can rapidly identify MDR infections and support early interventions. Include details of AI-driven screening, compound repurposing, and the design of antimicrobial peptides.

- Explores how genomics, proteomics, and metabolomics are analyzed using AI/ML to reveal new targets, highlights real-world AI solutions in diagnostics, treatment, and drug development.

- Discusses algorithmic bias, data privacy, and regulatory considerations in clinical AI applications.

This book is essential for researchers, healthcare professionals, data scientists, and graduate students in computer science, microbiology, pharmacology, and computational biology who are working to combat multidrug-resistant infections through interdisciplinary innovation.

Zielgruppe

Academic

Autoren/Hrsg.

Sharma, Tripti

Dewangan, Smriti

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Infektionskrankheiten
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie

Inhaltsverzeichnis

1. Introduction to Multi Drug Resistance Bacterial Infection. 2. Artificial Intelligence and Machine Learning in Antibiotic Discovery. 3. Artificial intelligence and machine learning for pathogen identification and antibiotic resistance detection. 4.Artificial Intelligence in the Development of Novel Antibacterial Agents. 5. Machine Learning (ML) applications in the field ofAntimicrobial Resistance (AMR). 6. AI in Antimicrobial Peptide Development. 7. AI Approaches to Antimicrobial Stewardship. 8. Artificial Intelligence Application in the Diagnosis and Treatment of Bacterial Infection. 9. Artificial Intelligence in Multi-drug-resistant Urinary Tract Infection. 10. Artificial Intelligence in Multi-drug-resistant Leprosy and Tuberculosis. 11. Artificial Intelligence in Microbial Natural Product Drug Discovery. 12. Machine learning in antibacterial drug design. 13. AI Governance, Legal Aspects, and Real-World Barriers in Antibacterial Drug Development. 14. Case studies on AI applications in combating Multidrug-Resistant infections.

Über Autor(innen)

Tripti Sharma is an Associate Professor at the Bombay College of Pharmacy, Mumbai, India, with over 22 years of experience in teaching and research in Pharmaceutical Chemistry, Medicinal Chemistry, Drug Development, and Pharmaceutical Analysis. She holds a Ph.D. in Pharmacy from Siksha ‘O’ Anusandhan University, Bhubaneswar, and an M. Pharm in Pharmaceutical Chemistry from BPUT, Odisha. Her research expertise includes medicinal chemistry, organic synthesis, computational drug design, molecular modeling, drug development, and analytical method development and validation. She has published 50 research articles, with a current Google Scholar H-index of 14 and an i10-index of 16. Her research focuses on the design and development of novel drug candidates, drug conjugates, computer-aided drug discovery, QSAR, molecular dynamics, and integrated computational and experimental approaches for therapeutic applications. She has contributed several book chapters and edited books with leading international publishers. Dr. Sharma has also served in several academic and administrative capacities. She is actively engaged in pharmaceutical research, teaching, academic administration, and professional activities.

Smriti Dewangan is an Associate Professor at Amity Institute of Pharmacy, Amity University Chhattisgarh, Raipur, India, with over 13 years of experience in teaching and research. She holds a Ph.D. from NIT Rourkela, supported by an Institute MHRD Fellowship, with expertise in organometallic chemistry and biological studies of organometallic compounds. She also holds an M. Pharm in Pharmaceutics through a GPAT fellowship and was a Gold Medalist in B. Pharm. She received the Young Scientist Award from ISCA in 2019. Her research focuses on the synthesis and biological evaluation of organometallic and medicinal compounds with anticancer, antioxidant, and antimicrobial potential, along with computational chemistry and drug design. She has 30+ research publications, 6+ book chapters, 3 books, and 2 patents to her credit and has contributed to several academic and professional publications with leading publishers. She has also received a DBT-CTEP grant for conducting a Popular Lecture Series and is a life member of several professional societies.