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.