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Computational Methods in Medicinal Chemistry, Pharmacology, and Toxicology
- 1st Edition - September 1, 2025
- Editor: Muhammad Ishfaq
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 0 2 4 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 0 2 5 - 4
"Computational Pharmacology and Toxicology" is a comprehensive book that provides a state-of-the-art overview of the field of computational methods in drug discovery, design, and… Read more
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Request a sales quote"Computational Pharmacology and Toxicology" is a comprehensive book that provides a state-of-the-art overview of the field of computational methods in drug discovery, design, and toxicity prediction. Organized into three parts, the book covers a wide range of topics in computational techniques and approaches, computer applications in pharmacology and toxicology, and future perspectives on new technologies. The coverage includes molecular modeling, virtual screening, machine learning, and network pharmacology as well as examples of how these methods can be applied to the discovery of new drugs, optimization of drug candidates, and assessment of drug safety and toxicity. It will be a valuable resource for researchers, practitioners, and students in the fields of pharmacology, toxicology, medicinal chemistry, bioinformatics, and systems biology.
• Offers comprehensive coverage of computational methods that are relevant to pharmacology and toxicology, including molecular modeling, virtual screening, machine learning, and network pharmacology.
• Includes practical examples and case studies that demonstrate how these methods can be applied in drug discovery, design, and toxicity prediction.
• Discusses emerging trends and future directions in the field of computational pharmacology and toxicology, which can help readers stay up to date with the latest advances and anticipate future developments.
• Includes practical examples and case studies that demonstrate how these methods can be applied in drug discovery, design, and toxicity prediction.
• Discusses emerging trends and future directions in the field of computational pharmacology and toxicology, which can help readers stay up to date with the latest advances and anticipate future developments.
Researchers and practitioners in the fields of pharmacology and toxicology who are interested in the application of computational methods to drug discovery, design, and toxicity prediction; Scientists working in the pharmaceutical industry involved in drug discovery and development, as well as in preclinical and clinical drug safety assessment; Academics and students and in the fields of pharmacology, toxicology, medicinal chemistry, bioinformatics, and systems biology who are interested in learning about the latest advances in computational methods and their applications in drug discovery and toxicology
PART I: COMPUTATIONAL TECHNIQUES AND APPROACHES
1. Introduction to Computational Methods in Pharmacology and Toxicology
2. Machine Learning Applications in Drug Discovery and Design
3. Exploring Deep Learning Applications in Drug Discovery and Design
4. Pattern Recognition, Molecular Descriptors, Quantum Mechanics, and Representation Methods
5. Exploring Databases Supporting Computational Pharmacology and Toxicology Techniques: An Overview
PART II: COMPUTER APPLICATIONS IN PHARMACOLOGY AND TOXICOLOGY: PHARMACEUTICAL, INDUSTRIAL, AND CLINICAL SETTINGS
6. QSAR and Pharmacophore Modeling in Computational Drug Design
7. Docking in Drug Discovery: Principles, Techniques, and Applications
8. In Silico Molecular Dynamics Simulations
9. Computational Techniques for Enhancing PK/PD Modeling and Simulation and ADMET prediction
10. Predictive Modeling in Toxicology: Unveiling Risks and Ensuring Safety
11. Integrated Network Analysis in Pharmacology: Decoding Interactions and Pathways for Therapeutic Insights
PART III: FUTURE PERSPECTIVES ON NEW TECHNOLOGIES IN PHARMACOLOGY AND TOXICOLOGY
12. An Overview of Computational Tools and Approaches for Green Molecular Design to Minimize Toxicological Risk in Chemical Compounds
13. Big Data in Computational Pharmacology and Toxicology, Challenges and Opportunities
14. Development of Next-Generation Tools for Advancing Computational Pharmacology and Toxicology
15. Ethical Considerations in Machine Learning and AI for Pharmacology and Toxicology
1. Introduction to Computational Methods in Pharmacology and Toxicology
2. Machine Learning Applications in Drug Discovery and Design
3. Exploring Deep Learning Applications in Drug Discovery and Design
4. Pattern Recognition, Molecular Descriptors, Quantum Mechanics, and Representation Methods
5. Exploring Databases Supporting Computational Pharmacology and Toxicology Techniques: An Overview
PART II: COMPUTER APPLICATIONS IN PHARMACOLOGY AND TOXICOLOGY: PHARMACEUTICAL, INDUSTRIAL, AND CLINICAL SETTINGS
6. QSAR and Pharmacophore Modeling in Computational Drug Design
7. Docking in Drug Discovery: Principles, Techniques, and Applications
8. In Silico Molecular Dynamics Simulations
9. Computational Techniques for Enhancing PK/PD Modeling and Simulation and ADMET prediction
10. Predictive Modeling in Toxicology: Unveiling Risks and Ensuring Safety
11. Integrated Network Analysis in Pharmacology: Decoding Interactions and Pathways for Therapeutic Insights
PART III: FUTURE PERSPECTIVES ON NEW TECHNOLOGIES IN PHARMACOLOGY AND TOXICOLOGY
12. An Overview of Computational Tools and Approaches for Green Molecular Design to Minimize Toxicological Risk in Chemical Compounds
13. Big Data in Computational Pharmacology and Toxicology, Challenges and Opportunities
14. Development of Next-Generation Tools for Advancing Computational Pharmacology and Toxicology
15. Ethical Considerations in Machine Learning and AI for Pharmacology and Toxicology
- No. of pages: 250
- Language: English
- Edition: 1
- Published: September 1, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443330247
- eBook ISBN: 9780443330254
MI
Muhammad Ishfaq
Dr. Muhammad Ishfaq is an Associate Professor at the College of Computer Science at Huanggang Normal University in Huanggang , China. His research areas of interest are In silico pharmacology, Machine learning, Computational Pharmacology and Toxicology, Drug targets, QSAR/QSPR modelling, and Cheminformatics. Dr. Ishfaq has published several research articles in various prestigious international journals with good impact factors. He is has also served as a volunteer reviewer for different prestigious journals. He is currently working on machine learning-based drug discovery and design and focusing on developing cutting edge technology on AI integrating biomedical big data searching for drugs targeting various diseases.
Affiliations and expertise
Associate Professor, College of Computer Science, Huanggang Normal University, Huanggang , China