1. Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
Morgan Thomas, Andrew Boardman, Miguel Garcia-Ortegon, Hongbin Yang, Chris de Graaf, and Andreas Bender
2. Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints
Andreas H. Göller, Lara Kuhnke, Antonius ter Laak, Katharina Meier, and Alexander Hillisch
3. Fighting COVID-19 with Artificial Intelligence
Stefania Monteleone, Tahsin F. Kellici, Michelle Southey, Michael J. Bodkin, and Alexander Heifetz
4. Application of Artificial Intelligence and Machine Learning in Drug Discovery
Rishi R. Gupta
5. Deep Learning and Computational Chemistry
Tim James and Dimitar Hristozov
6. Has Drug Design Augmented by Artificial Intelligence Become a Reality?
Atanas Patronov, Kostas Papadopoulos, and Ola Engkvist
7. Network Driven Drug Discovery
Jonny Wray and Alan Whitmore
8. Predicting Residence Time of GPCR Ligands with Machine Learning
Andrew Potterton, Alexander Heifetz, and Andrea Townsend-Nicholson
9. De Novo Molecular Design with Chemical Language Models
Francesca Grisoni and Gisbert Schneider
10. Deep Neural Networks for QSAR
Yuting Xu
11. Deep Learning in Structure-Based Drug Design
Andrew Anighoro
12. Deep Learning Applied to Ligand-Based De Novo Drug Design
Ferruccio Palazzesi and Alfonso Pozzan
13. Ultra-High Throughput Protein-Ligand Docking with Deep Learning
Austin Clyde
14. Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors
Tânia Cova, Carla Vitorino, Márcio Ferreira, Sandra Nunes, Paola Rondon-Villarreal, and Alberto Pais
15. Artificial Intelligence in Compound Design
Christoph Grebner, Hans Matter, and Gerhard Hessler
16. Artificial Intelligence, Machine Learning, and Deep Learning in Real Life Drug Design Cases
Christophe Muller, Obdulia Rabal Gracia, and Constantino Diaz Gonzalez
17. Artificial Intelligence-Enabled De Novo Design of Novel Compounds that are Synthesizable
Govinda Bhisetti and Cheng Fang
18. Machine Learning from Omics Data
René Rex
19. Deep Learning in Therapeutic Antibody Development
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes cutting-edge techniquesProvides step-by-step detail essential for reproducible resultsContains key implementation advice from the expertsThis volume looks at applications of artificial intelligence (AI), machine lear. N° de réf. du vendeur 732828053
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Writtenin the highly successfulMethods in Molecular Biologyseries format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls.Cutting-edge andunique,Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers. 544 pp. Englisch. N° de réf. du vendeur 9781071617892
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Taschenbuch. Etat : Neu. Artificial Intelligence in Drug Design | Alexander Heifetz | Taschenbuch | Methods in Molecular Biology | xi | Englisch | 2022 | Humana | EAN 9781071617892 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 125727998
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 544 pp. Englisch. N° de réf. du vendeur 9781071617892
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Writtenin the highly successfulMethods in Molecular Biologyseries format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls.Cutting-edge andunique,Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers. N° de réf. du vendeur 9781071617892
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