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Elsevier

Computational Methods in Medicinal Chemistry, Pharmacology, and Toxicology

  • 1st Edition - June 18, 2025
  • Latest edition
  • Editor: Muhammad Ishfaq
  • Language: English

Computational Methods in Medicinal Chemistry, Pharmacology, and Toxicology is a comprehensive resource that offers an advanced overview of computational techniques employed in dru… Read more

Description

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.

Key features

  • 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 that can help readers stay up-to-date with the latest advances and anticipate future developments

Readership

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

Table of contents

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

Product details

  • Edition: 1
  • Latest edition
  • Published: July 11, 2025
  • Language: English

About the editor

MI

Muhammad Ishfaq

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.

Affiliations and expertise
Visiting Research Scientist, Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan, USA

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