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Artificial Intelligence in Drug Discovery [Kõva köide]

Edited by (The Institute of Cancer Research, UK)
  • Formaat: Hardback, 424 pages, kõrgus x laius: 234x156 mm, kaal: 803 g, No
  • Sari: Drug Discovery Series Volume 75
  • Ilmumisaeg: 12-Nov-2020
  • Kirjastus: Royal Society of Chemistry
  • ISBN-10: 1788015479
  • ISBN-13: 9781788015479
Teised raamatud teemal:
  • Formaat: Hardback, 424 pages, kõrgus x laius: 234x156 mm, kaal: 803 g, No
  • Sari: Drug Discovery Series Volume 75
  • Ilmumisaeg: 12-Nov-2020
  • Kirjastus: Royal Society of Chemistry
  • ISBN-10: 1788015479
  • ISBN-13: 9781788015479
Teised raamatud teemal:
Due to significant advances in Deep Learning and related areas, artificial intelligence methods are increasingly utilised in drug discovery to tackle challenges that have hitherto been difficult to solve, such as predicting properties, designing molecules, and optimising synthetic routes. Artificial Intelligence in Drug Discovery comprehensively covers artificial intelligence and machine learning tools and techniques; covering specific challenges such as learning from chemical data, designing new molecular structures, predictive modelling in both ligand and structure-space, synthesis planning, and molecular simulations. The book tackles real-world challenges in drug discovery ensuring context of application is preserved and disseminated by world leaders in the field. Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation.
Introduction;

The History of Artificial Intelligence and Chemistry;

Chemical Topic Modelling An Unsupervised Approach Originating from
Text-mining to Organize Chemical Data;

Deep Learning and Chemical Data;

Concepts and Applications of Conformal Prediction in Computational Drug
Discovery;

Non-applicability Domain. The Benefits of Defining I dont know in
Artificial Intelligence;

Predicting Protein-Ligand Binding-Affinities;

Virtual Screening with Convolutional Neural Networks;

Machine Learning in the Area of Molecular Dynamics Simulations;

Compound Design Using Generative Neural Networks;

Junction Tree Variational Autoencoder for Molecular Graph Generation;

AI via Matched Molecular Pair Analysis;

Molecular de novo Design Through Deep Generative Models;

Active Learning for Drug Discovery and Automated Data Curation;

Data-driven Prediction of Organic Reaction Outcomes;

ChemOS: an Orchestration Software to Democratize Autonomous Discovery;

Summary and Outlook