Artificial Intelligence in Drug Design

Alexander Heifetz

ISBN 10: 1071617869 / ISBN 13: 9781071617861

Published by Springer US, Springer New York, 2021

About this Item

Druck auf Anfrage 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.

Seller Inventory # 9781071617861

Bibliographic Details

• Title: Artificial Intelligence in Drug Design
• Publisher: Springer US, Springer New York
• Publication Date: 2021
• Language: English
• ISBN 10: 1071617869
• ISBN 13: 9781071617861
• Binding: Buch
• Condition: Neu
• Dimensions: 260W x 183H x 35D millimeters 260 millimeters width by 183 millimeters height by 35 millimeters depth
• Weight: 1208 grams

About this title

Synopsis

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.