13 Prediction of antimicrobial activity using artificial intelligence
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Nitish Kumar Singh
Abstract
The fields of AI/ML (artificial intelligence/machine learning) have revolutionized the area of predicting antimicrobial activity, substantially enhancing the development of effective therapeutics for both animal and human health. These technologies have advanced the integration of multi-omics data, such as genomic, proteomic, and metabolomic datasets, and facilitated the development of a more precise and detailed forecasting framework. Modern ML methods, such as deep learning and neural networks, can now process enormous and complicated information to find new antibacterial agents and gauge their effectiveness. By simulating complex interactions between medications and microbial targets, these models can shed light on pharmacological modes of action, resistance mechanisms, and possible off-target consequences. AI-driven methods are also excellent at finding complementary medication combinations and maximizing polypharmacy tactics for difficult-to-treat illnesses. Furthermore, AI and ML facilitate the real-time processing of large extensive amounts of clinical and as well environmental data information, which helps guide the development of next-generation antimicrobial medicines and forecast the emergence of resistant strains. As AI and ML continue to advance, their role in predicting antimicrobial activity will be pivotal in combating infectious diseases, enhancing treatment efficacy, and improving global health outcomes for both humans and animals.
Abstract
The fields of AI/ML (artificial intelligence/machine learning) have revolutionized the area of predicting antimicrobial activity, substantially enhancing the development of effective therapeutics for both animal and human health. These technologies have advanced the integration of multi-omics data, such as genomic, proteomic, and metabolomic datasets, and facilitated the development of a more precise and detailed forecasting framework. Modern ML methods, such as deep learning and neural networks, can now process enormous and complicated information to find new antibacterial agents and gauge their effectiveness. By simulating complex interactions between medications and microbial targets, these models can shed light on pharmacological modes of action, resistance mechanisms, and possible off-target consequences. AI-driven methods are also excellent at finding complementary medication combinations and maximizing polypharmacy tactics for difficult-to-treat illnesses. Furthermore, AI and ML facilitate the real-time processing of large extensive amounts of clinical and as well environmental data information, which helps guide the development of next-generation antimicrobial medicines and forecast the emergence of resistant strains. As AI and ML continue to advance, their role in predicting antimicrobial activity will be pivotal in combating infectious diseases, enhancing treatment efficacy, and improving global health outcomes for both humans and animals.
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
