Buch, Englisch, Format (B × H): 152 mm x 229 mm
Buch, Englisch, Format (B × H): 152 mm x 229 mm
ISBN: 978-0-443-43274-3
Verlag: Elsevier Science & Technology
This volume surveys cutting-edge machine learning–based approaches for the study of enzyme catalysis, dynamics, and design. It brings together computational and experimental approaches that integrate molecular simulation, machine learning, and high-throughput data to interrogate enzymatic catalysis at multiple scales. The chapters in the volume highlight how data-driven frameworks complement physics-based models to advance mechanistic understanding and enable rational enzyme engineering.
Fachgebiete
Weitere Infos & Material
1. Inferring conformational equilibria from sparse experimental data
2. TBD
3. Gaussian Process Regression and DeepMD models for simulating solution-phase and enzyme reactions
4. Modern Computational Enzymology Methods for Nucleic Acid Catalysis
5. Capturing Transition States of Enzyme Dynamics
6. Deep learning approaches for identifying the reaction coordinates and mechanisms of enzyme catalysis
7. Methods of walking in path space as applied to chemical reactions in enzymes
8. TBD
9. Using large language models for enzyme kinetics data extraction
10. Multidimensional Mechanistic Profiling at Scale with High-Throughput Enzymology
11. TBD
12. Using CLEAN for new enzyme discovery
13. Using EZSpecificity to expand the substrate border.
14. GeoEvoBuilder-Driven Enzyme Engineering with Enhanced Activity and Thermostability




