Artificial Intelligence in Precision Drug Design, Volume 1
Foundations and Core Techniques
- 1st Edition - May 1, 2026
- Latest edition
- Editor: Khalid Raza
- Language: English
Foundations and Core Techniques of Bioinformatics in Precision Drug Design: Volume 1: Foundations and Core Techniques offers a comprehensive introduction to the transf… Read more
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Foundations and Core Techniques of Bioinformatics in Precision Drug Design: Volume 1: Foundations and Core Techniques offers a comprehensive introduction to the transformative role of AI in modern drug discovery. This volume lays the groundwork for understanding how machine learning, deep learning, and generative models are reshaping the development of precision therapeutics. The book explores foundational topics such as AI-driven molecular screening, pharmacokinetics, ADMET modeling, toxicity prediction, and omics integration. It also addresses ethical, philosophical, and epistemological dimensions, ensuring a well-rounded perspective. Each chapter, authored by global experts, combines theoretical insights with real-world case studies to bridge the gap between AI and life sciences. Designed for graduate students, researchers, and professionals in bioinformatics, biomedical sciences, and pharmaceutical R&D, this volume equips readers with essential knowledge and tools to navigate the evolving landscape of AI in drug design. It empowers interdisciplinary learners to apply cutting-edge AI techniques to real-world biomedical challenges.
- Presents foundational concepts in machine learning, deep learning, and cheminformatics, enabling readers to understand and apply AI techniques across the drug discovery pipeline
- Bridges disciplines through interdisciplinary insights, Connects life sciences, computer science, and bioinformatics, offering a structured entry point for researchers from diverse academic backgrounds
- Integrates real-world examples and applications to illustrate how AI tools are used in molecular screening, ADMET modeling, and pharmacokinetics prediction
researchers in: Biostatistics, Biomedical sciences, Bioinformatics and drug design
1. AI in Drug Design: A Historical and Future Perspective
2. Can Machines Truly Know? Epistemological Challenges in AI-Driven Drug Discovery
3. Ethical Implications of AI in Precision Drug Design: A Philosophical Inquiry
4. Metaphors of Medicine: A Literary Perspective on AI in Drug Discovery, Design and Target Precision
5. Artificial Intelligence in Molecular Screening: Advances, Challenges, and Future Perspectives
6. AI for Predicting Pharmacokinetics and Pharmacodynamics
7. AI for Predicting Drug-Likeness and Bioavailability
8. AI-Powered In Silico ADMET Modeling and Optimization in Drug Design
9. AI-Based Toxicity Prediction: Advancing Drug Safety and Risk Assessment
10. Leveraging AI for Integrating Genomics, Transcriptomics, and Proteomics
11. Artificial Intelligence in Multi-Omics Integration for Precision Drug Design
12. AI and Machine Learning for Disease Pathway Modelling
13. AI-Powered Genomic Medicine: Technologies and Challenges
14. PGP-Miner: An AI and Machine Learning Tool in Cancer Drug Development and Immunotherapy
15. Artificial Intelligence for Drug Repurposing: Opportunities and Challenges
16. Generative Artificial Intelligence for De-novo Drug Design
17. Bias and Transparency in AI and Machine Learning Models for Drug Design
18. Blockchain and AI in Drug Development: Securing Data Integrity and Transparency
19. Counterfactual Explainability in AI-Driven Drug Discovery: Enhancing Transparency and Decision-Making
20. Integrating AI in Pharmacovigilance and Clinical Trial Monitoring: Enhancing Drug Safety and Efficacy in Kyrgyzstan’s and LMIC’s Evolving Healthcare Landscape
2. Can Machines Truly Know? Epistemological Challenges in AI-Driven Drug Discovery
3. Ethical Implications of AI in Precision Drug Design: A Philosophical Inquiry
4. Metaphors of Medicine: A Literary Perspective on AI in Drug Discovery, Design and Target Precision
5. Artificial Intelligence in Molecular Screening: Advances, Challenges, and Future Perspectives
6. AI for Predicting Pharmacokinetics and Pharmacodynamics
7. AI for Predicting Drug-Likeness and Bioavailability
8. AI-Powered In Silico ADMET Modeling and Optimization in Drug Design
9. AI-Based Toxicity Prediction: Advancing Drug Safety and Risk Assessment
10. Leveraging AI for Integrating Genomics, Transcriptomics, and Proteomics
11. Artificial Intelligence in Multi-Omics Integration for Precision Drug Design
12. AI and Machine Learning for Disease Pathway Modelling
13. AI-Powered Genomic Medicine: Technologies and Challenges
14. PGP-Miner: An AI and Machine Learning Tool in Cancer Drug Development and Immunotherapy
15. Artificial Intelligence for Drug Repurposing: Opportunities and Challenges
16. Generative Artificial Intelligence for De-novo Drug Design
17. Bias and Transparency in AI and Machine Learning Models for Drug Design
18. Blockchain and AI in Drug Development: Securing Data Integrity and Transparency
19. Counterfactual Explainability in AI-Driven Drug Discovery: Enhancing Transparency and Decision-Making
20. Integrating AI in Pharmacovigilance and Clinical Trial Monitoring: Enhancing Drug Safety and Efficacy in Kyrgyzstan’s and LMIC’s Evolving Healthcare Landscape
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
KR
Khalid Raza
Dr. Khalid Raza is working as an Associate Professor at the Department of Computer Science, Jamia Millia Islamia, New Delhi. Earlier he worked as an “ICCR Chair Professor” at Ain Shams University, Cairo, Egypt. He has many years of teaching & research experiments in the field of Translational Bioinformatics and Computational Intelligence. He has contributed over 120 research articles in reputed Journals and Edited Books. Dr. Raza has authored/edited dozens of books published by reputed publishers. Dr. Raza is an Academic Editor of PeerJ Computer Science International Journal, and Guest Editor of the Journal Natural Product Communications. He has an active collaboration with the scientists from leading institutions of India and abroad. Recently, Dr. Raza has been featured in the list of Top 2% Scientists released by Stanford University (USA) in collaboration with Elsevier. His research interest lies in Machine Learning and its applications in Bioinformatics and Health-informatics.
Affiliations and expertise
Department of Computer Science, Jamia Millia Islamia (Central University), Jamia Nagar, New Delhi, India