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Table of Contents
Intro
Preface
Contents
Editor and Contributors
1 Machine Learning and Deep Learning Promote Computational Toxicology for Risk Assessment of Chemicals
1.1 Risk Assessment of Chemicals
1.2 Computational Toxicology
1.3 Machine Learning in Computational Toxicology
1.4 Deep Learning in Toxicology
1.5 Perspectives
References
Part I Machine Learning and Deep Learning Methods for Computational Toxicology
2 Assessment of the Xenobiotics Toxicity Taking into Account Their Metabolism
2.1 Introduction
2.2 Computational Methods of Studying Metabolism
2.2.1 Databases Containing Xenobiotic Metabolism Information
2.2.2 Descriptors/Notation Used for Metabolism Prediction
2.2.3 Prediction of Biotransformation Sites
2.2.4 Generation of the Structures of Probable Metabolites
2.2.5 Reactive Metabolite Formation Prediction
2.3 Integral Computational Assessment of Xenobiotic Toxicity
2.4 Future Directions in Xenobiotic Toxicity Assessment
References
3 Emerging Machine Learning Techniques in Predicting Adverse Drug Reactions
3.1 Introduction
3.2 Feature Generation for Machine Learning
3.2.1 Structure-Based Features
3.2.2 Interactions and Associations
3.2.3 Data Sources for Feature Generation
3.3 Conventional Methods for ADR Prediction
3.4 Emerging Methods for ADR Prediction
3.4.1 Molecule-Based Methods
3.4.2 Similarity-Based Methods
3.4.3 Network- and Graph-Based Methods
3.5 ADR Prediction Future Directions
References
4 Drug Effect Deep Learner Based on Graphical Convolutional Network
4.1 Introduction
4.2 Results
4.2.1 Gene Vector: Generation and Evaluation
4.2.2 Molecular Feature and Vector Generation
4.2.3 Cell Vector: Generation and Evaluation
4.2.4 Deep Drug Effect Predictor: Training and Validation
4.2.5 Application of DDEP to Predict the Effects of Anti-cancer Drugs Against Breast Adenocarcinoma
4.2.6 Insights into Drug Classification
4.3 Discussion
4.4 Methods
4.4.1 Capture Contextual Information of Genes from Their Interaction Networks
4.4.2 Generating Gene Vectors and Cell Vectors
4.4.3 GCN-Based Pre-models
4.4.4 Deep Drug Effect Predictor
References
5 AOP-Based Machine Learning for Toxicity Prediction
5.1 Introduction
5.2 Research Status and Existing Problems for ML
5.3 General Overview of AOP
5.3.1 The Generation of AOP
5.3.2 The Framework of AOP
5.3.3 Qualitative AOP and Quantitative AOP
5.4 Research Progress of Toxicity Prediction by AOP and ML
5.5 Perspectives and Future Prospects of AOP
References
6 Graph Kernel Learning for Predictive Toxicity Models
6.1 Introduction
6.2 A Brief Introduction of Graph Concepts
6.2.1 Graph Theory Definitions
6.2.2 Graph Kernels Fundamentals
6.3 Graph Kernel Learning for Molecular Representations
6.4 Applications of GKL Methods on Chemical Toxicity
Preface
Contents
Editor and Contributors
1 Machine Learning and Deep Learning Promote Computational Toxicology for Risk Assessment of Chemicals
1.1 Risk Assessment of Chemicals
1.2 Computational Toxicology
1.3 Machine Learning in Computational Toxicology
1.4 Deep Learning in Toxicology
1.5 Perspectives
References
Part I Machine Learning and Deep Learning Methods for Computational Toxicology
2 Assessment of the Xenobiotics Toxicity Taking into Account Their Metabolism
2.1 Introduction
2.2 Computational Methods of Studying Metabolism
2.2.1 Databases Containing Xenobiotic Metabolism Information
2.2.2 Descriptors/Notation Used for Metabolism Prediction
2.2.3 Prediction of Biotransformation Sites
2.2.4 Generation of the Structures of Probable Metabolites
2.2.5 Reactive Metabolite Formation Prediction
2.3 Integral Computational Assessment of Xenobiotic Toxicity
2.4 Future Directions in Xenobiotic Toxicity Assessment
References
3 Emerging Machine Learning Techniques in Predicting Adverse Drug Reactions
3.1 Introduction
3.2 Feature Generation for Machine Learning
3.2.1 Structure-Based Features
3.2.2 Interactions and Associations
3.2.3 Data Sources for Feature Generation
3.3 Conventional Methods for ADR Prediction
3.4 Emerging Methods for ADR Prediction
3.4.1 Molecule-Based Methods
3.4.2 Similarity-Based Methods
3.4.3 Network- and Graph-Based Methods
3.5 ADR Prediction Future Directions
References
4 Drug Effect Deep Learner Based on Graphical Convolutional Network
4.1 Introduction
4.2 Results
4.2.1 Gene Vector: Generation and Evaluation
4.2.2 Molecular Feature and Vector Generation
4.2.3 Cell Vector: Generation and Evaluation
4.2.4 Deep Drug Effect Predictor: Training and Validation
4.2.5 Application of DDEP to Predict the Effects of Anti-cancer Drugs Against Breast Adenocarcinoma
4.2.6 Insights into Drug Classification
4.3 Discussion
4.4 Methods
4.4.1 Capture Contextual Information of Genes from Their Interaction Networks
4.4.2 Generating Gene Vectors and Cell Vectors
4.4.3 GCN-Based Pre-models
4.4.4 Deep Drug Effect Predictor
References
5 AOP-Based Machine Learning for Toxicity Prediction
5.1 Introduction
5.2 Research Status and Existing Problems for ML
5.3 General Overview of AOP
5.3.1 The Generation of AOP
5.3.2 The Framework of AOP
5.3.3 Qualitative AOP and Quantitative AOP
5.4 Research Progress of Toxicity Prediction by AOP and ML
5.5 Perspectives and Future Prospects of AOP
References
6 Graph Kernel Learning for Predictive Toxicity Models
6.1 Introduction
6.2 A Brief Introduction of Graph Concepts
6.2.1 Graph Theory Definitions
6.2.2 Graph Kernels Fundamentals
6.3 Graph Kernel Learning for Molecular Representations
6.4 Applications of GKL Methods on Chemical Toxicity