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Table of contents
.1 Polypharmacology Modelling Using Proteochemometrics (PCM)
.1.1 Introduction
.1.1.1 Available bioactivity data is growing: but can we make sense of it?
.1.1.2 Synergy between ligand and target space
.1.1.3 PCM as a practical approach to use chemogenomics data
.1.1.4 Practical relevance of PCM
.1.2 Machine Learning in PCM
.1.2.1 Support Vector Machines (SVM)
.1.2.2 Random Forests (RF)
.1.2.3 Gaussian Processes (GP)
.1.2.4 Collaborative Filtering (CF)
.1.3 PCM Applied to Protein Target Families
.1.3.1 G protein-coupled receptors
.1.3.2 Kinases
.1.3.3 Histone modification and DNA methylation
.1.3.4 Viral mutants
.1.4 Novel Techniques and Applications in PCM
.1.4.1 Novel target similarity measure
.1.4.2 Including 3D information of protein targets in PCM
.1.4.3 PCM in predicting ligand binding free energy
.1.4.4 PCM as an approach to extrapolate bioactivity data between species
.1.4.5 PCM applied to pharmacogenomics and toxicogenomics data .
.1.4.6 Other potential PCM applications
.1.5 PCM Limitations
.1.6 Conclusions
.2 Predictive Bioactivity Modelling
.2.1 Compound standardization
.2.2 Descriptors
.2.2.1 Target descriptors
.2.2.2 Ligand descriptors
.2.2.3 Cross-term descriptors
.2.3 Statistical Preprocessing
.2.4 Generation of PCM Models
.2.5 Commonly used Algorithms
.2.6 Validation of PCM Models
.2.6.1 Statistical metrics
.2.7 Assessment of Maximum and Minimum Achievable Model Performance
.2.8 Conformal Prediction
.2.8.1 Regression
.2.8.2 Classification
Proteochemometric Modelling in a Bayesian Framework
.3 Proteochemometric Modelling in a Bayesian Framework
.3.1 Introduction
.3.2 Materials and Methods
.3.2.1 Data sets
.3.2.2 Descriptors
.3.2.3 Modelling with Bayesian inference
.3.2.4 Computational details
.3.2.5 Assessment of maximum model performance
.3.2.6 Interpretation of ligand substructures
.3.3 Results
.3.3.1 Model validation
.3.3.2 Predicted confidence intervals follow the cumulative density function of the Gaussian distribution
.3.3.3 Analysis of GP performance per target
.3.3.4 Model interpretation of ligand descriptors
.3.4 Discussion
.3.5 Conclusion
Benchmarking the Influence of Simulated Experimental Errors in QSAR
.4 Benchmarking the Influence of Simulated Experimental Errors in QSAR
.4.1 Introduction
.4.2 Materials and Methods
.4.2.1 Data sets
.4.2.2 Data sets
.4.2.3 Molecular Representation
.4.2.4 Molecular Representation
.4.2.5 Compound Descriptors
.4.2.6 Model generation
.4.2.7 Machine Learning Implementation
.4.2.8 Simulation of Noisy Bioactivities
.4.2.9 Experimental Design
.4.3 Results
.4.4 Discussion
Prediction of the Potency of Mammalian Cyclooxygenase Inhibitors with Ensemble Proteochemometric Modelling
.5 Isoform Selectivity Prediction: COX
.5.1 Introduction
.5.2 Materials and Methods
.5.2.1 Data set
.5.2.2 Descriptors
.5.2.3 Machine learning implementation
.5.2.4 Model generation
.5.2.5 Model validation
.5.2.6 Assessment of maximum model performance
.5.2.7 Ensemble modelling
.5.2.8 Estimation of the error of individual predictions
.5.2.9 Interpretation of compound substructures
.5.3 Results
.5.3.1 Analysis of the chemical and the target space
.5.3.2 PCM validation
.5.3.3 PCM models are in agreement with the maximum achievable performance
.5.3.4 PCM outperforms both Family QSAR and Family QSAM on this data set
.5.3.5 PCM outperforms individual QSAR models
.5.3.6 Model ensembles exhibit higher performance than single PCM models
.5.3.7 The ensemble standard deviation enables the definition of informative confidence intervals
.5.3.8 Ensemble modelling enables the prediction of uncorrelated human COX inhibitor bioactivity profiles
.5.3.9 Model performance per target is related to compound diversity
.5.3.10 Interpretation of compound substructures
.5.4 Discussion
.5.5 Conclusion
Large-scale Cancer Cell-Line Sensitivity Prediction
.6 Large-scale prediction of growth inhibition paerns on the NCI60 cancer cell-line panel
.6.1 Introduction
.6.2 Materials and Methods
.6.2.1 Data sets
.6.2.2 Compound descriptors
.6.2.3 Compound clustering
.6.2.4 Model generation
.6.2.5 Model validation
.6.2.6 Conformal prediction
.6.2.7 Pathway-drug associations
.6.2.8 Comparison to previous methods
.6.3 Results
.6.3.1 Summary of the cell-line profiling data set views
.6.4 Discussion
Bibliography
