Artificial Intelligence in Precision Drug Design, Volume 1
Foundations and Core Techniques
- 1st Edition - February 19, 2026
- Latest edition
- Editor: Khalid Raza
- Language: English
Artificial Intelligence in Precision Drug Design: Advanced Applications showcases how artificial intelligence (AI) is revolutionizing modern drug discovery and development.… Read more
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Description
Description
Artificial Intelligence in Precision Drug Design: Advanced Applications showcases how artificial intelligence (AI) is revolutionizing modern drug discovery and development. Building upon the foundational principles established in Volume 1, this book dives into real-world applications where AI accelerates innovation, enhances predictive accuracy, and enables breakthrough therapeutics. Featuring contributions from leading global researchers and practitioners, the book explores machine learning, deep learning, and network-based approaches applied to complex biomedical challenges. Key areas include AI driven drug repurposing, combination therapies, immunotherapy, vaccine design, quantum computing, and the integration of large language models in drug discovery. Additional chapters highlight predictive modeling using electronic health records, AI-powered medical imaging, and explainable AI for structure-based drug design. What sets this volume apart is its emphasis on practical impact, demonstrating how data, computation, and interdisciplinary collaboration converge to advance precision medicine. Designed for scientists, clinicians, educators, and students, it serves as both a comprehensive reference and a source of inspiration for leveraging AI to transform healthcare.
Key features
Key features
• Focuses on real-world AI applications in drug design and development provides comprehensive coverage of advanced techniques: deep learning, network-based models, and quantum computing.
• Includes case studies on drug repurposing, combination therapies, immunotherapy, and vaccine development.
• Provides insights into predictive modeling, AI-driven medical imaging, and explainable AI.
• Highlights practical impact and interdisciplinary collaboration in precision medicine.
• Includes case studies on drug repurposing, combination therapies, immunotherapy, and vaccine development.
• Provides insights into predictive modeling, AI-driven medical imaging, and explainable AI.
• Highlights practical impact and interdisciplinary collaboration in precision medicine.
Readership
Readership
researchers in: Biostatistics, Biomedical sciences, Bioinformatics and drug design
Table of contents
Table of contents
1. AI in drug design: A historical and future perspective
Ulfat Andrabi, Tairah Andrabi, Aaliya Ashraf and Syed Mohd Khalid
2. Can machines truly know? Epistemological challenges in AI-driven drug discovery
Oviemuno Wilfred Egara, Peter M. Etaware, Blessing Oboli and Henry Chinenye Ogbonna
3. Ethical implications of AI in precision drug design: A philosophical inquiry
Oviemuno Wilfred Egara, Peter M. Etaware, Blessing Oboli and Ekene Michael Mokwenye
4. Metaphors of medicine: a literary perspective of the involvement of AI in drug discovery, design, and target precision
Blessing Oboli, Peter M. Etaware and Oviemuno Wilfred Egara
5. Artificial intelligence in molecular screening: Advances, challenges, and future perspectives
Junjie Kuang, Shalesh Gangwar, Shaban Ahmad and Khalid Raza
6. AI for predicting pharmacokinetics and pharmacodynamics
Amrita Thakur, S. Anil Kumar and Sreeharsha Nagaraja
7. Artificial Intelligence for predicting drug-likeness and bioavailability
Bikash Kumar Rajak and Priyanka Rani
8. AI-powered In silico ADMET modeling and optimization in drug design
Anil Kumar Sasidharan Pillai, Amrita Thakur and Sreeharsha Nagaraja
9. AI-based toxicity prediction: Advancing drug safety and risk assessment
R. Karwasra, K. Khanna, I. Sharma, S. Singh and M.S. Rajagopal
10. Leveraging AI for integrating genomics, transcriptomics, and proteomics
Manoj Kumar Jana, Deepesh Joshi, Deepika, Anami Ahuja, Piyal Mukherjee, Achal Kumar Srivastava, Mahesh Narayan, Sudip Das, Sahar Qazi, Neeraj Mohan Gupta, Prajwal Panth and Vishnu Swarup
11. Artificial intelligence in multi-omics integration for precision drug design
Nagmi Bano, Saima Firdaus, Rafat Parveen, Shaban Ahmad and Khalid Raza
12. AI and machine learning for disease pathway modeling [case study: malaria infection]
Peter Mudiaga Etaware, Victor Segun Ekun, Oviemuno Wilfred Egara, Blessing Oboli, Ebruphihor Nina Odafe and Elizabeth Ufuoma Etaware
13. AI-powered genomic medicine: Technologies and challenges
G. Ravindran, R. Swami, S. Dugad, T.N. Nighitha, S. Kanskar, F.J.B. Mendonça Junior, M.T. Scotti, L. Scotti and A. Nayarisseri
14. PGP-miner: An AI and machine learning tool in cancer drug development and immunotherapy Sarra Akermi, Abira Dey, Ruoya Li, Nathalie Larzat, Jean Bernard Idoipe and Ashwani Sharma
15. Artificial intelligence for drug repurposing: Opportunities and challenges
Saira Hamid, Raiyan Ali, Mogana S. Rajagopal, Shalesh Gangwar and Khalid Raza
16. Generative artificial intelligence for de novo drug design
Ahmed Mohsin Ali, Sahar Qazi, Haider Thaer Abdulhameed Almuqdadi, Neeraj, Osamah S. Majeed, Alabbas Abdulkareem Majeed, Ahmed Abd Temur, Ghassan Haleem Mohsin Al Murshedi and Mohammed Saleem Waheed
17. Bias and transparency inAI and machine learning models for drug design
Wasswa Shafik
18. Blockchain and AI in drug development: Securing data integrity and transparency
Syed Mohd Khalid, Tairah Andrabi, Rakesh Mahajan and Khushleen Kaur
19. Counterfactual explainability in AI-driven drug discovery: Enhancing transparency and decision-making
Abdullahi Isa, Souley Boukari and Muhammad Aliyu
20. Integrating AI in pharmacovigilance and clinical trial monitoring: Enhancing drug safety and efficacy in Kyrgyzstan’s and LMIC’s evolving healthcare landscape
Mohd Faizan Siddiqui, Azaroual Mouna, Roman Kalmatov, Kozhobekov Kudayberdi Gaparalievich, Gulnara Isaeva, Kudaiberdieva Gulmira Karimovna, Azaroual Ghezlane, Zhanbaeva Anara Kenesovna, Imetova Jazgul Bukarbaevna, Matkasymova Aijan Tashpolotova, Mamazhanova Syrga Alimbekovna, Sydikov Akmal Abdikahharovich, Omorova Aizhan, Abdimomunova Begimai Toktobolotovna, Ali Mirza, Ali Usman, Kadyrkulova Dzamila Uzgenova, Shaikh Sanjitha Banu, Bekbolot Arinbaev, Fayozidinovich Muidinov Fazliddin, Tugolbay Mamaev, Turdaliev Samatbek Orozalievich, Asanbek Kanymgul Kyzy and P. Timur
Ulfat Andrabi, Tairah Andrabi, Aaliya Ashraf and Syed Mohd Khalid
2. Can machines truly know? Epistemological challenges in AI-driven drug discovery
Oviemuno Wilfred Egara, Peter M. Etaware, Blessing Oboli and Henry Chinenye Ogbonna
3. Ethical implications of AI in precision drug design: A philosophical inquiry
Oviemuno Wilfred Egara, Peter M. Etaware, Blessing Oboli and Ekene Michael Mokwenye
4. Metaphors of medicine: a literary perspective of the involvement of AI in drug discovery, design, and target precision
Blessing Oboli, Peter M. Etaware and Oviemuno Wilfred Egara
5. Artificial intelligence in molecular screening: Advances, challenges, and future perspectives
Junjie Kuang, Shalesh Gangwar, Shaban Ahmad and Khalid Raza
6. AI for predicting pharmacokinetics and pharmacodynamics
Amrita Thakur, S. Anil Kumar and Sreeharsha Nagaraja
7. Artificial Intelligence for predicting drug-likeness and bioavailability
Bikash Kumar Rajak and Priyanka Rani
8. AI-powered In silico ADMET modeling and optimization in drug design
Anil Kumar Sasidharan Pillai, Amrita Thakur and Sreeharsha Nagaraja
9. AI-based toxicity prediction: Advancing drug safety and risk assessment
R. Karwasra, K. Khanna, I. Sharma, S. Singh and M.S. Rajagopal
10. Leveraging AI for integrating genomics, transcriptomics, and proteomics
Manoj Kumar Jana, Deepesh Joshi, Deepika, Anami Ahuja, Piyal Mukherjee, Achal Kumar Srivastava, Mahesh Narayan, Sudip Das, Sahar Qazi, Neeraj Mohan Gupta, Prajwal Panth and Vishnu Swarup
11. Artificial intelligence in multi-omics integration for precision drug design
Nagmi Bano, Saima Firdaus, Rafat Parveen, Shaban Ahmad and Khalid Raza
12. AI and machine learning for disease pathway modeling [case study: malaria infection]
Peter Mudiaga Etaware, Victor Segun Ekun, Oviemuno Wilfred Egara, Blessing Oboli, Ebruphihor Nina Odafe and Elizabeth Ufuoma Etaware
13. AI-powered genomic medicine: Technologies and challenges
G. Ravindran, R. Swami, S. Dugad, T.N. Nighitha, S. Kanskar, F.J.B. Mendonça Junior, M.T. Scotti, L. Scotti and A. Nayarisseri
14. PGP-miner: An AI and machine learning tool in cancer drug development and immunotherapy Sarra Akermi, Abira Dey, Ruoya Li, Nathalie Larzat, Jean Bernard Idoipe and Ashwani Sharma
15. Artificial intelligence for drug repurposing: Opportunities and challenges
Saira Hamid, Raiyan Ali, Mogana S. Rajagopal, Shalesh Gangwar and Khalid Raza
16. Generative artificial intelligence for de novo drug design
Ahmed Mohsin Ali, Sahar Qazi, Haider Thaer Abdulhameed Almuqdadi, Neeraj, Osamah S. Majeed, Alabbas Abdulkareem Majeed, Ahmed Abd Temur, Ghassan Haleem Mohsin Al Murshedi and Mohammed Saleem Waheed
17. Bias and transparency inAI and machine learning models for drug design
Wasswa Shafik
18. Blockchain and AI in drug development: Securing data integrity and transparency
Syed Mohd Khalid, Tairah Andrabi, Rakesh Mahajan and Khushleen Kaur
19. Counterfactual explainability in AI-driven drug discovery: Enhancing transparency and decision-making
Abdullahi Isa, Souley Boukari and Muhammad Aliyu
20. Integrating AI in pharmacovigilance and clinical trial monitoring: Enhancing drug safety and efficacy in Kyrgyzstan’s and LMIC’s evolving healthcare landscape
Mohd Faizan Siddiqui, Azaroual Mouna, Roman Kalmatov, Kozhobekov Kudayberdi Gaparalievich, Gulnara Isaeva, Kudaiberdieva Gulmira Karimovna, Azaroual Ghezlane, Zhanbaeva Anara Kenesovna, Imetova Jazgul Bukarbaevna, Matkasymova Aijan Tashpolotova, Mamazhanova Syrga Alimbekovna, Sydikov Akmal Abdikahharovich, Omorova Aizhan, Abdimomunova Begimai Toktobolotovna, Ali Mirza, Ali Usman, Kadyrkulova Dzamila Uzgenova, Shaikh Sanjitha Banu, Bekbolot Arinbaev, Fayozidinovich Muidinov Fazliddin, Tugolbay Mamaev, Turdaliev Samatbek Orozalievich, Asanbek Kanymgul Kyzy and P. Timur
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 20, 2026
- Language: English
About the editor
About the editor
KR
Khalid Raza
Dr. Khalid Raza is an Associate Professor at the Department of Computer Science, Jamia Millia Islamia, New Delhi, India, and an Adjunct Professor at UCSI University, Malaysia. He has over 14 years of teaching and research experience and previously served as an ICCR Chair Visiting Professor at Ain Shams University, Egypt. Dr. Raza has published more than 160 peer-reviewed papers and authored/edited over 15 books with Springer, Elsevier, and CRC Press. He serves as Academic Editor of PLoS ONE, BMC Artificial Intelligence, and Guest Editor of npj Precision Oncology, JoVE, and several other journals. Recipient of Clarivate’s (Web of Science) India Excellence Research Citation Award 2025, Dr. Raza is consistently featured in the World’s Top 2% Scientists list (2022–2025). His research focuses on AI, bioinformatics, and health informatics
Affiliations and expertise
Department of Computer Science, Jamia Millia Islamia (Central University), Jamia Nagar, New Delhi, IndiaView book on ScienceDirect
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