Computational Chemistry

Author(s)
Shahidul M. Islam
Edition
1
Copyright
2026
Pages
124
Book Type
Academic

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CHOOSE YOUR FORMAT
Help Me Choose

Paperback Book

$85.00

ISBN: 9798319720993
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eBook

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ISBN: 9798319721013
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Electronic Delivery EBOOK - 365 days

This textbook provides a hands-on, application-driven introduction to computational chemistry, molecular modeling, and artificial intelligence (AI) methods used in modern chemical, biochemical, and pharmaceutical research. Through guided tutorials and practical exercises, students learn how to apply important software tools and machine learning approaches to solve real-world problems in molecular science and biomedical research.

A significant portion of the book focuses on molecular dynamics (MD) simulations, including system preparation using CHARMM-GUI, MD simulation execution using the AMBER software suite, and trajectory analysis for evaluating protein stability, flexibility, and intermolecular interactions. Students also gain practical experience performing MM/GBSA binding free energy calculations using MD trajectory data.

The book also introduces molecular visualization and structural analysis using ChimeraX, allowing students to understand biomolecular structures and protein–ligand interactions. It then introduces structure-based drug discovery workflows through molecular docking using AutoDock Vina and machine learning–based prediction of binding affinity and dissociation constants (KD) for protein–ligand complexes.

The book further introduces quantum chemistry concepts through hands-on tutorials using GaussView and Gaussian 16, enabling students to build molecular structures, calculate molecular properties such as bond lengths and bond angles, and perform reaction free energy calculations.

The final section expands into biomedical data science by introducing machine learning approaches to predict disease onset, demonstrating how computational chemistry and AI tools can be applied to translational biomedical challenges.

Designed for upper-level undergraduate and graduate courses, this textbook is ideal for chemistry, biochemistry, pharmaceutical sciences, computational biology, and data science programs. The tutorial-based structure makes it particularly suitable for laboratory courses, computational workshops, and interdisciplinary training programs focused on AI-enabled molecular design and drug discovery.

By integrating molecular modeling, physics-based simulation methods, quantum chemistry, and machine learning workflows, this textbook equips students with the practical computational skills required for careers in academia, biotechnology, pharmaceuticals, and data-driven molecular science.

Tutorial 1 Visualize Protein-Ligand Interactions with VMD

Tutorial 2 Molecular Visualization and Analysis with ChimeraX

Tutorial 3 Molecular Docking to a Protein using Autodock Vina

Tutorial 4 Calculation of Absolute Binding Affinity of a ligand to a Protein Using KDEEP , a Machine Learning Approach

Tutorial 5 Using CHARMM-GUI to Prepare Molecular Dynamics Simulation Files to Run MD Simulation with AMBER

Tutorial 6 Molecular Dynamics (MD) Simulation Conducted with AMBER

Tutorial 7 Analysis and Interpretation of MD Simulation Trajectory Files

Tutorial 8 MM/GBSA Binding Free Energy Calculations with MD Simulation Trajectories

Tutorial 9 Building and Modifying Molecules with GaussView

Tutorial 10 Basic Quantum Mechanical Calculations with Gaussian

Tutorial 11 Free Energy Calculation of a Chemical Reaction Using Gaussian16

Tutorial 12 Predicting the Onset Age of Disease (Diabetes) Using ML Model

Shahidul M. Islam

Dr. Shahidul Islam is an Associate Professor in the Department of Chemistry at Delaware State University, with extensive teaching experience in general chemistry, biochemistry, and computational chemistry. His research focuses on understanding biological macromolecules particularly proteins—and their interactions with small molecules, peptides, and other proteins for biotechnological applications. He integrates state-of-the-art computational approaches with in vitro experiments to address challenges in infectious diseases, cancer, food allergens, and genetic disorders.

Dr. Islam has published over fifty articles in leading journals, including Nature, Cell, Proceedings of the National Academy of Sciences, and American Chemical Society journals, and has secured funding from agencies such as National Science Foundation, National Institutes of Health, and United States Department of Agriculture.

He previously served as a Research Assistant Professor at the University of Illinois Chicago (2016–2022) and as a postdoctoral researcher at the University of Chicago (2011–2015). He earned his MBA from the University of Illinois at Chicago in 2022 and his Ph.D. in Chemistry from Memorial University of Newfoundland in 2008.

This textbook provides a hands-on, application-driven introduction to computational chemistry, molecular modeling, and artificial intelligence (AI) methods used in modern chemical, biochemical, and pharmaceutical research. Through guided tutorials and practical exercises, students learn how to apply important software tools and machine learning approaches to solve real-world problems in molecular science and biomedical research.

A significant portion of the book focuses on molecular dynamics (MD) simulations, including system preparation using CHARMM-GUI, MD simulation execution using the AMBER software suite, and trajectory analysis for evaluating protein stability, flexibility, and intermolecular interactions. Students also gain practical experience performing MM/GBSA binding free energy calculations using MD trajectory data.

The book also introduces molecular visualization and structural analysis using ChimeraX, allowing students to understand biomolecular structures and protein–ligand interactions. It then introduces structure-based drug discovery workflows through molecular docking using AutoDock Vina and machine learning–based prediction of binding affinity and dissociation constants (KD) for protein–ligand complexes.

The book further introduces quantum chemistry concepts through hands-on tutorials using GaussView and Gaussian 16, enabling students to build molecular structures, calculate molecular properties such as bond lengths and bond angles, and perform reaction free energy calculations.

The final section expands into biomedical data science by introducing machine learning approaches to predict disease onset, demonstrating how computational chemistry and AI tools can be applied to translational biomedical challenges.

Designed for upper-level undergraduate and graduate courses, this textbook is ideal for chemistry, biochemistry, pharmaceutical sciences, computational biology, and data science programs. The tutorial-based structure makes it particularly suitable for laboratory courses, computational workshops, and interdisciplinary training programs focused on AI-enabled molecular design and drug discovery.

By integrating molecular modeling, physics-based simulation methods, quantum chemistry, and machine learning workflows, this textbook equips students with the practical computational skills required for careers in academia, biotechnology, pharmaceuticals, and data-driven molecular science.

Tutorial 1 Visualize Protein-Ligand Interactions with VMD

Tutorial 2 Molecular Visualization and Analysis with ChimeraX

Tutorial 3 Molecular Docking to a Protein using Autodock Vina

Tutorial 4 Calculation of Absolute Binding Affinity of a ligand to a Protein Using KDEEP , a Machine Learning Approach

Tutorial 5 Using CHARMM-GUI to Prepare Molecular Dynamics Simulation Files to Run MD Simulation with AMBER

Tutorial 6 Molecular Dynamics (MD) Simulation Conducted with AMBER

Tutorial 7 Analysis and Interpretation of MD Simulation Trajectory Files

Tutorial 8 MM/GBSA Binding Free Energy Calculations with MD Simulation Trajectories

Tutorial 9 Building and Modifying Molecules with GaussView

Tutorial 10 Basic Quantum Mechanical Calculations with Gaussian

Tutorial 11 Free Energy Calculation of a Chemical Reaction Using Gaussian16

Tutorial 12 Predicting the Onset Age of Disease (Diabetes) Using ML Model

Shahidul M. Islam

Dr. Shahidul Islam is an Associate Professor in the Department of Chemistry at Delaware State University, with extensive teaching experience in general chemistry, biochemistry, and computational chemistry. His research focuses on understanding biological macromolecules particularly proteins—and their interactions with small molecules, peptides, and other proteins for biotechnological applications. He integrates state-of-the-art computational approaches with in vitro experiments to address challenges in infectious diseases, cancer, food allergens, and genetic disorders.

Dr. Islam has published over fifty articles in leading journals, including Nature, Cell, Proceedings of the National Academy of Sciences, and American Chemical Society journals, and has secured funding from agencies such as National Science Foundation, National Institutes of Health, and United States Department of Agriculture.

He previously served as a Research Assistant Professor at the University of Illinois Chicago (2016–2022) and as a postdoctoral researcher at the University of Chicago (2011–2015). He earned his MBA from the University of Illinois at Chicago in 2022 and his Ph.D. in Chemistry from Memorial University of Newfoundland in 2008.

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