This second edition volume expands on the previous edition with discussions on the latest advancements in artificial intelligence (AI) applications in protein-drug interaction studies, and describes applications of different computational methodologies for drug discovery and creating efficient docking workflows using Jupyter Notebooks. The chapters in this book cover topics such as AlphaFold; AI models to address protein-ligand interactions; machine-learning models to predict binding affinity based on the atomic coordinates of protein-ligand complexes; AutoDock Vina; Molegro Virtual Docker (MVD); and workflows integrating docking engines and machine-learning techniques to build regression models and apply them to drug discovery. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and thorough, Docking Screens for Drug Discovery, Second Edition is a valuable reference for all researchers interested in learning more about the development of computational tools for drug discovery.

Includes cutting-edge methods and protocols Provides step-by-step detail essential for reproducible results Contains key implementation advice from the experts

Inhalt

A Primer of SanDReS 2.0 for Scoring Function Design.- Exploring the Scoring Function Space with Lasso Regression.- Combining MVD and Ridge Method to Predict CDK2 Inhibition.- Elastic Net Regression to Predict CDK2 Inhibition.- Gradient Descent to Predict Enzyme Inhibition.- Decision Tree for Prediction of Binding Affinity.- Calculating Enzyme Inhibition with Random Forests.- Extremely Randomized Trees to Determine Binding Affinity.- Hands-On Docking with Molegro Virtual Docker.- Molegro Virtual Docker for Docking Screens.- Molegro Data Modeller for Machine Learning.- Neural Networks with Molegro Data Modeller.- AlphaFold for Docking Screens.- Differential Evolution for Docking Simulations.- Machine Learning to Predict CDK4 Inhibition.- Targeting CDK9 with Molegro Virtual Docker.- CDK7 as a Target for Docking Screens.- Molegro Data Modeller to Estimate CDK6 Inhibition.- Neural Networks to Calculate CDK2 Inhibition.- Tree-Based Methods to Predict Enzyme Inhibition.