Computational Methods in Medicinal Chemistry, Pharmacology, and Toxicology is a comprehensive resource that offers an advanced overview of computational techniques employed in drug discovery, design, and toxicity prediction. The book discusses various topics, including molecular modeling, virtual screening, machine learning, and network pharmacology. It serves as an essential guide for researchers, practitioners, and students in pharmacology, toxicology, medicinal chemistry, bioinformatics, and systems biology fields, showcasing practical applications and future perspectives on new technologies. In addition to covering computational approaches, the book provides real-world examples of drug discovery, candidate optimization, and safety assessment.
Other sections explore computer applications in pharmacology and toxicology and discusses the importance of these methods in advancing medicinal research.
Part I: Computational Techniques and Approaches
1. Introduction to Computational Methods in Medicinal Chemistry, Pharmacology
and Toxicology
2. Applications of Machine Learning for Advanced 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 Medicinal Chemistry, 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 Medicinal Chemistry,
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 Medicinal Chemistry, Pharmacology and
Toxicology, Challenges and Opportunities
14. Development of Next-Generation Tools for Advancing Computational
Medicinal Chemistry, Pharmacology and Toxicology
15. Ethical Considerations in Machine Learning and AI for Medicinal
Chemistry, Pharmacology and Toxicology
Muhammad Ishfaq holds a Doctor of Veterinary Medicine (DVM), a Masters degree (MSc), and a PhD in basic veterinary medicine, specializing in Veterinary Pharmacology and Toxicology. He was previously an associate professor at Huanggang Normal University, Hubei, China. He is currently a visiting research scientist at the Department of Medicinal Chemistry, University of Michigan in Ann Arbor, Michigan, USA. His research areas of interest are in silico pharmacology, machine learning, computational pharmacology and toxicology, drug targets, QSAR/QSPR modeling, and cheminformatics. Dr. Ishfaq has published several research articles in various prestigious international journals. He has also served as a volunteer reviewer for various international prestigious and peer-reviewed journals. He is currently working on the connection of diseases to specific bio-targets using various cell and tissue cultures, proteomics, bioinformatics, imaging studies, machine learning-based drug discovery and design and the development of cutting-edge technology on AI integrating biomedical big data that search for drugs targeting various diseases to save endangered species. More specifically, he works at the interface of veterinary pharmacology, toxicology, medicinal chemistry, and biodiversity conservation.
