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Automating drug target discovery with machine learning

Slides presented at ODSC Europe (London, October 2017)

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Automating drug target discovery with machine learning

  1. 1. Automating drug target discovery with machine learning Enrico Ferrero, PhD, Associate GSK Fellow Scientific Leader, Computational Biology, Target Sciences, GSK ODSC Europe 13.10.2017 @enricoferrero
  2. 2. Data is the new oil Yahoo Finance & Forbes, 2017 The Economist, 2017
  3. 3. Data + AI = drugs? BBC News, 2017 Nature Biotechnology, 2017
  4. 4. The pharma AI space is getting crowded Partner Partner
  5. 5. Developing a new drug: 15+ years, $2B+
  6. 6. Challenging times for pharma R&D
  7. 7. So, what’s wrong? Harrison, Nat Rev Drug Discov, 2016 Cook et al., Nat Rev Drug Discov, 2014
  8. 8. Late phase failures cost (a lot) more Manhattan Institute, 2012
  9. 9. Rethink the drug discovery pipeline
  10. 10. But how do we find good targets? Nelson et al., Nat Genet, 2015
  11. 11. Open Targets Koscielny et al., 2016
  12. 12. Could it be as easy as spotting spam emails? ▪ Is it possible to predict novel therapeutic targets using available gene – disease association data? ▪ Is Open Targets just a catalogue of gene – disease associations or can we learn from it what makes a good target?
  13. 13. A positive – unlabelled (PU) semi- supervised learning approach ▪ Obtain all gene – disease associations and supporting evidence from Open Targets platform. For all genes, create numeric features by taking the mean score across all diseases: ▪ Genetic associations (germline) ▪ Somatic mutations ▪ Significant gene expression changes ▪ Disease-relevant phenotype in animal model ▪ Pathway-level evidence ▪ Gather positive labels from Pharmaprojects: only consider targets with drugs currently on the market, in clinical trials or preclinical studies. A semi-supervised framework with only positive labels is used: targets according to PharmaProjects constitute the positive class (P), while the rest of the proteome is used as the unlabelled class (U), containing both negatives and yet-to-be-discovered positive. ▪ All positive cases (1421) and an equal number of randomly selected unlabelled cases (2842 in total) are set apart for training (80%) and testing (20%). The remainder is kept as a prediction set where predictions from the final model will be made.
  14. 14. 14 Finding structure in the data Hierarchical clustering PCA t-SNE
  15. 15. Identifying most important features Chi-squared test and information gain Decision tree classification criteria
  16. 16. Nested cross-validation and bagging for tuning and model selection Bischl et al., 2012 Wikipedia Four classifiers are independently tuned, trained and tested on the training set using a nested cross-validation strategy (4 inner rounds for parameter tuning and 4 outer rounds to assess performance): ▪ Random forest ▪ Feed-forward neural network with single hidden layer ▪ Support vector machine with radial kernel ▪ Gradient boosting machine with AdaBoost exponential loss function In PU learning, U contains both positive and negative cases, which results in classifier instability. Bagging (bootstrap aggregating) can improve the performance of instable classifiers by randomly resampling P and U with replacement (bootstrap) and then aggregating the results by majority voting: ▪ Bagging with 100 iterations was applied to the neural network, the support vector machine and the gradient boosting machine. ▪ Random forests are already a special case of bagging.
  17. 17. Assessing classifier performance Neural network classifier achieves 71% accuracy (0.76 AUC) on test set
  18. 18. Investigating results across the pipeline Successful and more advanced targets have higher disease association evidence
  19. 19. Validation of predictions with literature mining Significant overlap between neural network predictions and text mining results (p = 5.05e-172)
  20. 20. Automating drug target discovery with machine learning ▪ The gene – disease association data from Open Targets contains enough information to predict whether a protein can make a therapeutic target or not with decent accuracy. ▪ According to our model, the most informative evidence types are animal models showing disease-relevant phenotypes, dysregulated gene expression in disease tissue and genetic associations between gene and disease. ▪ The ability to predict late stage targets with greater accuracy confirms that clear linkage between target and disease is essential to maximise chances of success in the clinic. ▪ Limitations: ▪ Lack of prediction on indication; ▪ No tractability considerations.
  21. 21. Thank you! ▪ Philippe Sanseau ▪ Ian Dunham ▪ Gautier Koscielny ▪ Giovanni Dall’Olio ▪ Pankaj Agarwal ▪ Mark Hurle ▪ Steven Barrett ▪ Nicola Richmond ▪ Jin Yao

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