Artificial Intelligence in Drug Discovery /

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.

Chi tiết về thư mục
Tác giả khác:	Brown, Nathan
Định dạng:	Licensed eBooks
Ngôn ngữ:	Tiếng Anh
Được phát hành:	Cambridge : Royal Society of Chemistry, 2020.
Loạt:	RSC drug discovery
Truy cập trực tuyến:	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2668239

Mục lục:

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
Chapter 5 Concepts and Applications of Conformal Prediction in Computational Drug Discovery
5.1 Introduction
5.2 Conformal Prediction Modalities Commonly Used in Computer-aided Drug Design
5.2.1 Inductive Conformal Prediction (ICP)
5.3 Handling Imbalanced Datasets: Mondrian Conformal Prediction (MCP)
5.3.1 ICP for Regression
5.3.2 Conformal Prediction Using All Labelled Data for Learning
5.4 Conformal Prediction Methods for Deep Learning
5.5 Open-source Implementations of Conformal Prediction
5.6 Current Limitations of Conformal Prediction and Future Perspectives
Conflicts of Interest
References
Chapter 6 Non-applicability Domain. The Benefits of Defining "I Don't Know" in Artificial Intelligence
6.1 Introduction
6.2 Predictive Models
6.3 Defining NotAvailable Predictions
6.4 All Leave One Out Models
6.5 Benefits of Defining NotAvailable Predictions
6.6 Simulation Study
6.6.1 Design of the Experiment
6.6.2 Results of the Experiment

Artificial Intelligence in Drug Discovery /

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