AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms
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A. Muniyappan
Abstract
AI that has not been integrated with preclinical testing experimental methods is another effective means through which medicationmedication development can be hastened. This book chapter examines the several uses of AI in preclinical testing, namely as target and validation identifications, hit identification, molecular docking, lead optimization, toxicity assessment, and biomarker identification. For the purpose of uncovering the disease mechanisms and novel therapeutic measures, the omics information, capabilities of predicting the drug activities and properties, enhancing screening assays, and fusing multiple dimensions of data are all tackled by machine learning. AI tools help scientists identify more effective and safer drug candidates, enhance the implementation of precision medicine, enhance the accuracy, sensitivity, and variability of preclinical testing assays. This work can help understand how AI can change the process of creating drugsdrugs and provides a detailed evaluation of the current state of AI implementation during preclinical studies.
Abstract
AI that has not been integrated with preclinical testing experimental methods is another effective means through which medicationmedication development can be hastened. This book chapter examines the several uses of AI in preclinical testing, namely as target and validation identifications, hit identification, molecular docking, lead optimization, toxicity assessment, and biomarker identification. For the purpose of uncovering the disease mechanisms and novel therapeutic measures, the omics information, capabilities of predicting the drug activities and properties, enhancing screening assays, and fusing multiple dimensions of data are all tackled by machine learning. AI tools help scientists identify more effective and safer drug candidates, enhance the implementation of precision medicine, enhance the accuracy, sensitivity, and variability of preclinical testing assays. This work can help understand how AI can change the process of creating drugsdrugs and provides a detailed evaluation of the current state of AI implementation during preclinical studies.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
