Artificial intelligence in drug discovery

✍ Scribed by Nathan Brown (editor)

• Publisher: Royal Society of Chemistry
• Year: 2021
• Tongue: English
• Leaves: 416
• Series: Drug discovery series
• Category: Library

✦ Table of Contents

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
16.3.3 Early Realization of Self-driving Laboratories
16.4 ChemOS to Orchestrate Next-generation Experimentation
16.4.1 AI-aided Experiment Planning
16.4.2 Intuitive Human–Robot Interactions
16.4.3 Online Analysis of Experimental Results
16.4.4 Databases Management and Storage Solutions
16.4.5 Connection to Automated Solutions
16.5 Successful Applications of ChemOS to Scientific Challenges
16.5.1 Calibration of an Automated Setup for Real-time Reaction Monitoring
16.5.2 Discovery of Conductive Thin-film Materials
16.5.3 Formulation of Polymer Blends for Photo-stable Solar Cells
16.6 Application of ChemOS to Drug Discovery
16.7 Conclusion and Outlook
References
Section 8: Summary and Outlook
Chapter 17 Summary and Outlook
17.1 Introduction
17.2 Challenges
17.2.1 Data
17.2.2 Compute
17.2.3 Culture
17.3 Summary
Subject Index