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020 _a9781071649497
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024 7 _a10.1007/978-1-0716-4949-7
_2doi
050 4 _aRM300-666
072 7 _aMMG
_2bicssc
072 7 _aMED071000
_2bisacsh
072 7 _aMKG
_2thema
082 0 4 _a615
_223
245 1 0 _aDocking Screens for Drug Discovery
_h[electronic resource] /
_cedited by Walter Filgueira de Azevedo Jr.
250 _a2nd ed. 2026.
264 1 _aNew York, NY :
_bSpringer US :
_bImprint: Humana,
_c2026.
300 _aXIV, 314 p. 87 illus., 83 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
341 0 _bPDF/UA-1
_2onix
341 0 _bTable of contents navigation
_2onix
341 0 _bSingle logical reading order
_2onix
341 0 _bShort alternative textual descriptions
_2onix
341 0 _bUse of color is not sole means of conveying information
_2onix
341 0 _bUse of high contrast between text and background color
_2onix
341 0 _bNext / Previous structural navigation
_2onix
341 0 _bAll non-decorative content supports reading without sight
_2onix
347 _atext file
_bPDF
_2rda
490 1 _aMethods in Molecular Biology,
_x1940-6029 ;
_v2984
505 0 _aA 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.
520 _aThis 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.
532 8 _aAccessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub.
532 8 _aNo reading system accessibility options actively disabled
532 8 _aPublisher contact for further accessibility information: accessibilitysupport@springernature.com
650 0 _aPharmacology.
650 0 _aBioinformatics.
650 1 4 _aPharmacology.
650 2 4 _aComputational and Systems Biology.
700 1 _ade Azevedo Jr., Walter Filgueira.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9781071649480
776 0 8 _iPrinted edition:
_z9781071649503
776 0 8 _iPrinted edition:
_z9781071649510
830 0 _aMethods in Molecular Biology,
_x1940-6029 ;
_v2984
856 4 0 _uhttps://doi.org/10.1007/978-1-0716-4949-7
912 _aZDB-2-PRO
999 _c8458
_d8458