Comprehensive simulation methods for studying biomolecular interactions and drug design

Understanding how proteins interact with ligands, peptides, and nucleic acids requires sophisticated computational approaches. Computational Modeling of Biomolecular Interactions: Methods and Applications delivers authoritative coverage of simulation techniques for characterizing interaction structures, energetics, kinetics, pathways, and mechanisms. Expert contributors provide both theoretical foundations and practical applications for researchers investigating molecular recognition and drug binding.

The book covers quantum mechanics / molecular mechanics (QM/MM), molecular docking, Brownian dynamics, molecular dynamics (MD), and enhanced sampling methods including supervised MD, dissipation-corrected targeted MD, weighted ensemble, replica exchange, metadynamics, Gaussian accelerated MD, and more. Detailed chapters address binding free energy calculations, drug binding kinetics, and machine learning and deep learning applications. Application studies examine protein-ligand, protein-peptide, protein-protein, and protein-DNA/RNA interactions, plus conformational changes, allostery, gene editing mechanisms, and structure-based drug design.

Readers will also find:

• Step-by-step guidance on implementing accelerated MD and enhanced sampling methods for overcoming timescale limitations in biomolecular simulations
• Practical protocols for calculating binding free energies and characterizing drug binding kinetics essential for rational drug design workflows
• Integration of machine learning and deep learning approaches with traditional simulation methods for improved prediction accuracy and efficiency
• Application case studies demonstrating computational analysis of conformational changes, allosteric mechanisms, and gene editing protein function
• Coverage spanning fundamental simulation theory through advanced applications relevant to biochemistry, pharmacology, and computational chemistry research

Researchers in biochemistry, computational chemistry, pharmacology, biophysics, and chemical biology will find this volume an authoritative resource for computational studies of biomolecular interactions. The combination of methodological depth and practical applications makes it valuable for both method development and applied drug discovery research.

Yinglong Miao, PhD, is an Associate Professor in the Department of Pharmacology, Computational Medicine Program and Lineberger Comprehensive Cancer Center at University of North Carolina - Chapel Hill. His research focuses on computational methods for studying biomolecular interactions, including molecular dynamics simulations, enhanced sampling techniques, and machine learning approaches for drug discovery and biomolecular dynamics characterization.