Artificial Intelligence in Drug Discovery / edited by Nathan Brown

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..

Intro -- Title -- Copyright -- Contents -- Section 1: Introduction to Artificial Intelligence and Chemistry -- Chapter 1 Introduction -- 1.1 Introduction -- Section 2: Chemical Data -- Chapter 2 The History of Artificial Intelligence and Chemistry -- 2.1 Artificial Intelligence in History -- 2.2 The Winters of Artificial Intelligence -- 2.3 Chemistry Finding Artificial Intelligence -- 2.4 Synthesis Planning -- 2.5 Predictive Modelling of Properties -- 2.6 Summary -- References -- Chapter 3 Chemical Topic Modeling - An Unsupervised Approach Originating from Text-mining to Organize Chemical Data -- 3.1 Introduction -- 3.2 Topic Modeling and LDA -- 3.2.1 The Mathematical Framework of LDA -- 3.2.2 Advanced Topic Modeling Extensions -- 3.2.3 Topic Modeling and Its Relation to Other Machine Learning Methods -- 3.2.4 Topic Modeling in Different Scientific Disciplines -- 3.3 Chemical Topic Modeling -- 3.3.1 Feature Representation for Chemical Topic Modeling -- 3.3.2 Creating and Interpreting a Chemical Topic Model -- 3.3.3 Evaluation of a Chemical Topic Model -- 3.4 Exploring Large Data Sets with Chemical Topic Modeling -- 3.4.1 Hierarchical Topics -- 3.5 Combining Text and Chemical Information -- 3.6 Conclusions, Limitations and Future Work -- References -- Chapter 4 Deep Learning and Chemical Data -- 4.1 Introduction -- 4.2 Background -- 4.2.1 Deep Learning -- 4.2.2 Evaluation Methods -- 4.2.3 Natural-language Processing -- 4.3 Case Study 1: Spectroscopic Analysis -- 4.3.1 Background -- 4.3.2 Worked NMR Example -- 4.4 Case Study 2: Natural Language Processing Experiments -- 4.4.1 Introduction -- 4.4.2 Chemical Entity Mentions in Patents -- 4.4.3 Deep Learning vs. Feature Engineering for Relationship Extraction -- 4.5 Conclusions and Future Work -- References -- Section 3: Ligand-based Predictive Modelling..

Medienart:	E-Book

Erscheinungsjahr:	2021 ©2021
Erschienen:	Cambridge: Royal Society of Chemistry ; 2021 ©2021

Reihe:	#75#Drug discovery series - 75

Sprache:	Englisch

Beteiligte Personen:	Brown, Nathan [bherausgeberin]

Links:	ebookcentral.proquest.com [lizenzpflichtig]

ISBN:	978-1-83916-054-7

BKL:	44.42 / Pharmazeutische Chemie
Themen:	Artificial intelligence-Medical applications Electronic books

Anmerkungen:	Description based on publisher supplied metadata and other sources

Umfang:	1 online resource (416 pages)

Förderinstitution / Projekttitel:

PPN (Katalog-ID):	1738853357