Artificial Intelligence in Drug Design

Alexander Heifetz

ISBN 10: 1071617893 / ISBN 13: 9781071617892

Verlag: Springer US, Springer US Nov 2022, 2022

Beschreibung

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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 544 pp. Englisch.

Bestandsnummer des Verkäufers 9781071617892

Bibliografische Details

• Titel: Artificial Intelligence in Drug Design
• Verlag: Springer US, Springer US Nov 2022
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 1071617893
• ISBN-13: 9781071617892
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 254B x 178 H x 30 T Millimeter Breite: 254 mm, Höhe: 178 mm, Tiefe: 30 mm
• Gewicht: 1011 Gramm

Über diesen Titel

Inhaltsangabe

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. 

Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.