Docking Screens for Drug Discovery

Walter Filgueira de Azevedo Jr. (Herausgeber)

Buch | Hardcover

314 Seiten

2025 | Second Edition 2026
Humana (Verlag)
978-1-0716-4948-0 (ISBN)

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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.

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.

Erscheinungsdatum	17.10.2025
Reihe/Serie	Methods in Molecular Biology
Zusatzinfo	83 Illustrations, color; 4 Illustrations, black and white
Sprache	englisch
Maße	178 x 254 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Informatik ► Weitere Themen ► Bioinformatik
	Medizin / Pharmazie ► Medizinische Fachgebiete ► Pharmakologie / Pharmakotherapie
	Medizin / Pharmazie ► Pharmazie
	Naturwissenschaften ► Biologie
Schlagworte	Artificial Intelligence • Docking simulations • machine learning • Pharmaceutical databases • Protein-ligand docking
ISBN-10	1-0716-4948-5 / 1071649485
ISBN-13	978-1-0716-4948-0 / 9781071649480
Zustand	Neuware