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.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Brown, Nathan (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, English : Royal Society of Chemistry, [2021]
Colección:RSC drug discovery series.
Temas:
Drugs > Design > Computer simulation.
Artificial intelligence > Medical applications.
Médicaments > Conception > Simulation par ordinateur.
Intelligence artificielle en médecine.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 6.6.3 Discussion
  • 6.7 Questions and Criticism
  • 6.7.1 Question 1
  • 6.7.2 Question 2
  • 6.7.3 Question 3
  • 6.7.4 Question 4
  • 6.7.5 Question 5
  • 6.7.6 Question 6
  • 6.7.7 Question 7
  • 6.7.8 Question 8
  • 6.8 Final Remarks
  • Abbreviations
  • References
  • Section 4: Structure-based Predictive Modelling
  • Chapter 7 Predicting Protein-ligand Binding Affinities
  • 7.1 Introduction
  • 7.2 A Brief Background on Classical Methodologies
  • 7.2.1 Potential-based
  • 7.2.2 Simulation-based
  • 7.2.3 Data-based
  • 7.3 Modern Machine-learning Scoring Functions
  • 7.3.1 Domain Applicability
  • 7.3.2 Descriptors
  • 7.3.3 Models
  • 7.3.4 Interpretability
  • 7.3.5 Implementation and Availability
  • 7.4 Available Data and Evaluation
  • 7.4.1 Scope and Databases
  • 7.4.2 Evaluation
  • 7.5 Discussion
  • References.
  • Chapter 8 Virtual Screening with Convolutional Neural Networks
  • 8.1 Introduction
  • 8.1.1 Virtual Screening
  • 8.1.2 Traditional Approaches to Virtual Screening
  • 8.1.3 Machine Learning Scoring Functions
  • 8.1.4 Rationale for Deep Learning Approaches
  • 8.2 Virtual Screening
  • 8.2.1 Data Sets for Structure-based Virtual Screening
  • 8.2.2 Appropriate Train/Test Splits for SBVS
  • 8.2.3 Evaluation Measures
  • 8.3 Convolutional Neural Networks
  • 8.3.1 CNNs: A Primer
  • 8.3.2 ImageNet
  • 8.3.3 Modern CNN Architectures
  • 8.4 CNN Applications for Virtual Screening
  • 8.4.1 Input Format for CNN Structure-based Virtual Screening
  • 8.4.2 Outline and Performance of CNN-based Methods
  • 8.5 Other Closely Related Tasks
  • 8.5.1 Pose Prediction
  • 8.5.2 Binding Affinity Prediction
  • 8.6 Visualisation
  • 8.7 Outlook
  • References
  • Chapter 9 Machine Learning in the Area of Molecular Dynamics Simulations
  • 9.1 Introduction
  • 9.1.1 Basics of Molecular Dynamics
  • 9.1.2 Machine-learning Applications
  • 9.1.3 MD and ML
  • 9.2 Using Machine Learning to Improve Force Fields
  • 9.2.1 Multi-variate Linear Regression
  • 9.2.2 Bayesian Inference
  • 9.2.3 Genetic Algorithm
  • 9.2.4 Random Forest Regression
  • 9.2.5 Artificial Neural Network
  • 9.2.6 Remarks
  • 9.3 Improving Sampling in MD Simulations
  • 9.3.1 General Sampling Enhancement
  • 9.3.2 Estimating the Biasing Potential for a Given Reaction Coordinate
  • 9.3.3 Estimating Optimal Collective Variables
  • 9.4 Learning from MD Trajectories
  • 9.4.1 Application to Clustering
  • 9.4.2 Application to Property Prediction
  • 9.4.3 Application to Kinetic Models
  • 9.5 Perspectives and Challenges
  • 9.5.1 Datasets on Dynamics Information
  • 9.5.2 Benchmarking
  • 9.5.3 Open-source Implementation
  • 9.5.4 Concluding Remarks
  • References
  • Section 5: Molecular Design.
  • Chapter 10 Compound Design Using Generative Neural Networks
  • 10.1 Introduction
  • 10.2 Principles of Deep Learning
  • 10.3 De Novo Design via Deep Learning
  • 10.3.1 Molecular Representation
  • 10.3.2 Recurrent Neural Networks
  • 10.3.3 Autoencoder Variants
  • 10.3.4 Graph-based Neural Networks
  • 10.4 Property Prediction through Deep Learning
  • 10.5 Conclusions and Outlook
  • References
  • Chapter 11 Junction Tree Variational Autoencoder for Molecular Graph Generation
  • 11.1 Introduction
  • 11.2 Neural Generation of Molecular Graphs
  • 11.2.1 Junction Tree
  • 11.2.2 Tree and Graph Encoder
  • 11.2.3 Junction Tree Decoder
  • 11.2.4 Graph Decoder
  • 11.3 Application to Molecular Design
  • 11.3.1 Molecular Generative Model
  • 11.3.2 Molecule-to-Molecule Translation
  • 11.4 Experiments
  • 11.4.1 Molecular Variational Autoencoder
  • 11.4.2 Molecular Translation
  • 11.5 Conclusion
  • References
  • Chapter 12 AI via Matched Molecular Pair Analysis
  • 12.1 Introduction
  • 12.2 Essential Features of Artificial Intelligence
  • 12.3 Matched Molecular Pair Analysis
  • 12.3.1 Generic Issues in Identifying Matched Molecular Pairs
  • 12.3.2 Automation
  • 12.3.3 Other Matched Pair Technologies
  • 12.3.4 Fuzzy Matched Pairs
  • 12.3.5 Matched Molecular Series
  • 12.3.6 MMPA Enhanced by Protein Structural Data
  • 12.4 Future Developments
  • 12.5 Summary
  • References
  • Chapter 13 Molecular De Novo Design Through Deep Generative Models
  • 13.1 Introduction
  • 13.2 Sequence-based Methods for De Novo Generation of Small Molecules
  • 13.2.1 Embeddings and Tokenization
  • 13.2.2 Recurrent Neural Networks
  • 13.2.3 Sampling SMILES from RNNs
  • 13.2.4 Properties and Synthesizability
  • 13.2.5 Advanced Neural Architectures
  • 13.3 Graph-based De Novo Structure Generation
  • 13.4 Benchmarking Generative Molecular De Novo Design Models.
  • 13.4.1 Benchmarking Explorative Models
  • 13.4.2 Benchmarking Exploitative Models
  • 13.4.3 Benchmarking Models During Training
  • 13.4.4 Comparing Model Architectures
  • 13.5 Conclusions
  • References
  • Chapter 14 Active Learning for Drug Discovery and Automated Data Curation
  • 14.1 Introduction
  • 14.2 Active Learning for Drug Discovery, Chemistry, and Material Science
  • 14.2.1 Exploitation vs. Exploration
  • 14.2.2 Balancing Different Objectives
  • 14.2.3 When to Stop
  • Say When!
  • 14.2.4 Batch Selection
  • 14.2.5 Benchmarking the Learning
  • 14.3 Active Learning for Data Curation
  • 14.3.1 Reduced Redundancy and Balanced Data
  • 14.3.2 Reactive Learning
  • 14.4 Conclusions
  • References
  • Section 6: Synthesis Planning
  • Chapter 15 Data-driven Prediction of Organic Reaction Outcomes
  • 15.1 Introduction
  • 15.1.1 The Role of Reaction Prediction
  • 15.1.2 Non-data Driven Heuristic Systems
  • 15.2 Data-driven Approaches
  • 15.2.1 Focused Analyses of Specific Reaction Classes
  • 15.2.2 At the Mechanistic Level
  • 15.2.3 Via Reaction Templates
  • 15.2.4 Without Reaction Templates: Graphs
  • 15.2.5 Without Reaction Templates: Sequences
  • 15.3 Conclusion
  • 15.3.1 Data Availability
  • 15.3.2 Evaluation
  • 15.3.3 Breadth versus Accuracy
  • 15.4 Model Types
  • 15.5 Conclusion
  • References
  • Section 7: Future Outlook
  • Chapter 16 ChemOS: An Orchestration Software to Democratize Autonomous Discovery
  • 16.1 Introduction
  • 16.2 Automated Approaches to Scientific Discovery
  • 16.2.1 Algorithmic Strategies to Screen the Parameter Space
  • 16.2.2 Examples of Automation in Key Industrial Sectors and Academia
  • 16.2.3 Limitations of Automated Approaches
  • 16.3 Autonomous Approaches to Scientific Discovery
  • 16.3.1 Algorithmic Strategies to Experiment Planning
  • 16.3.2 Roadmap for Deploying and Orchestrating the Self-driving Laboratories.

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