Muhammad Ishfaq holds a Doctor of Veterinary Medicine (DVM), a Master’s 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.

• Part I: Computational Techniques and Approaches1. Introduction to Computational Methods in Medicinal Chemistry, Pharmacology and Toxicology2. Applications of Machine Learning for Advanced Drug Discovery and Design3. Exploring Deep Learning Applications in Drug Discovery and Design4. Pattern Recognition, Molecular Descriptors, Quantum Mechanics, and Representation Methods5. Exploring Databases Supporting Computational Pharmacology and Toxicology Techniques: An OverviewPart II: Computer Applications in Medicinal Chemistry, Pharmacology and Toxicology: Pharmaceutical, Industrial, and Clinical Settings6. QSAR and Pharmacophore Modeling in Computational Drug Design7. Docking in Drug Discovery: Principles, Techniques, and Applications8. In Silico Molecular Dynamics Simulations9. Computational Techniques for Enhancing PK/PD Modeling and Simulation and ADMET prediction10. Predictive Modeling in Toxicology: Unveiling Risks and Ensuring Safety11. Integrated Network Analysis in Pharmacology: Decoding Interactions and Pathways for Therapeutic InsightsPart III: Future Perspectives on New Technologies in Medicinal Chemistry, Pharmacology and Toxicology12. An Overview of Computational Tools and Approaches for Green Molecular Design to Minimize Toxicological Risk in Chemical Compounds13. Big Data in Computational Medicinal Chemistry, Pharmacology and Toxicology, Challenges and Opportunities14. Development of Next-Generation Tools for Advancing Computational Medicinal Chemistry, Pharmacology and Toxicology15. Ethical Considerations in Machine Learning and AI for Medicinal Chemistry, Pharmacology and Toxicology