Keywords: biosignatures, machine learning, drug design, data fusion, high-throughput screening
Webpages:
https://www.openanalytics.eu/ For decades, high throughput screening of chemical compounds has played a central role in drug design. In general, such screens were only affordable if they had a narrow biological scope (e.g., compound activity on an isolated protein target). In recent years, screening techniques have become available that combine a high throughput with a high dimensional readout and a complex biological context (e.g., cell culture). Examples are high content imaging and L1000 transcriptomics. In addition, due to state-of-the-art machine learning methods (Unterthiner et al. 2014) and high performance computing (Harnie et al. 2016) it has become possible to benefit from such high dimensional biological data on an enterprise scale. Together, these advances enable
Biosignature-Based Drug Design, a paradigm that will dramatically change pharmaceutical research.
A software pipeline, mainly built in
R and
C++, allows us to support Biosignature-Based Drug Design in an enterprise setting. It is worth noting that dealing with multiple data sets of this scale and complexity is non-trivial and challenging. With our pipeline, we tailor generic methods to the needs of specific projects in diverse therapeutic areas. This operational application goes hand in hand with an ongoing effort –together with academic partners– to improve and extend our workflow.
We will show use cases in which Biosignature-Based Drug Design has increased the effectiveness and cost-efficiency of high throughput screens by repurposing historic data (Simm et al. 2017). Moreover, integrating multiple data sources allows to takes into account a broader biological context, rather than a single mode of action. This will yield a better understanding of on- and off-target effects. Ultimately, this may reduce failure rates for drug candidates in clinical trials.
Acknowledgements This work was supported by research grants IWT130405 ExaScience Life Pharma and IWT150865 Exaptation from the Flanders Innovation and Entrepreneurship agency.
References Harnie, D., M. Saey, A. E. Vapirev, J.K. Wegner, A. Gedich, M.N. Steijaert, H. Ceulemans, R. Wuyts, and W. De Meuter. 2016. “Scaling Machine Learning for Target Prediction in Drug Discovery Using Apache Spark.”
Future Generation Computer Systems.
Simm, J., G. Klambauer, A. Arany, M.N. Steijaert, J.K. Wegner, E. Gustin, V. Chupakhin, et al. 2017. “Repurposed High-Throughput Images Enable Biological Activity Prediction for Drug Discovery.”
bioRxiv.
Unterthiner, T., A. Mayr, G. Klambauer, M.N. Steijaert, H. Ceulemans, J.K. Wegner, and S. Hochreiter. 2014. “Deep Learning as an Opportunity in Virtual Screening.” In
Workshop on Deep Learning and Representation Learning (Nips 2014).
