1) The document discusses using machine learning to automate drug target discovery by analyzing gene-disease association data from Open Targets. 2) A neural network classifier was able to predict therapeutic targets with 71% accuracy by analyzing features like genetic associations, somatic mutations, animal models, and pathway evidence from Open Targets. 3) The most informative features for prediction were animal models showing disease-relevant phenotypes, dysregulated gene expression in disease tissue, and genetic associations between genes and diseases.

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. Data is the new oil
Yahoo Finance & Forbes, 2017
The Economist, 2017

3. Data + AI = drugs?
BBC News, 2017 Nature Biotechnology, 2017

4. The pharma AI space is getting crowded
Partner
Partner

5. Developing a new drug: 15+ years, $2B+

6. Challenging times for pharma R&D

7. So, what’s wrong?
Harrison, Nat Rev Drug Discov, 2016
Cook et al., Nat Rev Drug Discov, 2014

8. Late phase failures cost (a lot) more
Manhattan Institute, 2012

9. Rethink the drug discovery pipeline

10. But how do we find good targets?
Nelson et al., Nat Genet, 2015

11. Open Targets
Koscielny et al., 2016

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. 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
Finding structure in the data
Hierarchical clustering PCA t-SNE

15. Identifying most important features
Chi-squared test and information gain Decision tree classification criteria

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. Assessing classifier performance
Neural network classifier
achieves 71% accuracy
(0.76 AUC) on test set

18. Investigating results across the pipeline
Successful and more advanced targets have higher
disease association evidence

19. Validation of predictions with literature mining
Significant overlap between neural
network predictions and text mining
results (p = 5.05e-172)

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. Thank you!
▪ Philippe Sanseau
▪ Ian Dunham
▪ Gautier Koscielny
▪ Giovanni Dall’Olio
▪ Pankaj Agarwal
▪ Mark Hurle
▪ Steven Barrett
▪ Nicola Richmond
▪ Jin Yao