Includes cutting-edge methods and protocols

Provides step-by-step detail essential for reproducible results

Contains key notes and implementation advice from the experts

Computer-Aided Drug Design - An Overview.- Prediction of Human Drug Targets and their Interactions Using Machine Learning Methods: Current and Future Perspectives.- Practices in Molecular Docking and Structure-based Virtual Screening.- Phylogenetic and Conservation Based Approaches to Predict Protein Functional Sites.-
De novo
design of Ligands using Computational Methods.- Molecular Dynamics Simulation and the Prediction of Druggable Binding Sites.- Virtual Ligand Screening using PL-PatchSurfer2, a Molecular Surface-based Protein-ligand Docking Method.- Fragment Based Ligand Designing.- Molecular Dynamics as a Tool for Virtual Ligand Screening.- Building Molecular Interaction Networks from Microarray Data for Drug Target Screening.- Absolute Alchemical Free Energy Calculations for Ligand Binding.- Evaluation of Protein-ligand Docking by Cyscore.- Molecular Dynamics Simulations of Protein-drug Complexes: A Computational Protocol for Investigating the Interactions of Small-Molecule Therapeutics with Biological Targets and Biosensors.- Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand.- Protein-protein Docking in Drug Design and Discovery.- Automated Inference of Chemical Discriminants of Biological Activity.- Computational Exploration of Conformational Transitions in Protein Drug Targets.- Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design.- Calculation of Thermodynamic Properties of Bound Water Molecules.- Enhanced Molecular Dynamics Methods Applied to Drug Design Projects.- AGGRESCAN3D: Towards the Prediction of the Aggregation Propensities of Protein Structures.- Computational Analysis of Solvent Inclusion in Docking Studies of Protein-glycosaminoglycan Systems.- Understanding G Protein-Coupled Receptor Allostery
via
Molecular Dynamics Simulations: Implications for Drug Discovery.- Identification of Potential Microrna Biomarkers by Meta-analysis.