I recently gave a talk at Neo4J's developer conference called "Graph-Based Features for Recommendation Systems in Drug Discovery" (It was at 7 am in the morning, so the link is below if you missed it and want to watch 😄).
The talk gave me a great opportunity for a technical presentation to a wide audience, and explain how we at Crossr see recommendation systems supercharging biologists and speeding up the drug discovery cycle. The talk had a technical focus on the application of Neo4J (the leading vendor for graph databases), a technology exploding as the value of connected data becomes realised.
Recommendation systems have seen success across various companies such as Netflix for movie recommendations or e-commerce companies for surfacing ads, but have not yet been widely applied in drug discovery. At Crossr, we see them as hugely beneficial in helping scientists make key prioritisation decisions fast.
The first use case that comes to mind is identifying a gene (or protein) as a drug target to treat a disease. There are often thousands of potential targets for a scientist to consider for a disease, and prioritising which genes to move forward with is a difficult process that can take months. A recommendation system provides a mechanism for the scientist to decide what features/characteristics are important to them and quickly apply them in ranking thousands of genes. This process results in a short list (maybe 20 genes) that can then be manually reviewed and pre-clinically validated.
This is just one example, but recommendation systems can be applied in other areas including drug repurposing and clinical trials (the list goes on!).
Graph-based approaches and recommendation systems have a bright future in drug discovery, but it's an evolving field so open discussion is needed to push adoption forward. Therefore, I invite anyone to comment on my presentation here, or get in touch if you want to chat about it.
A Whitepaper using our platform to identify targets for Epilepsy similar to published literature with the speed and efficiency gains of software.
Our view on the potential of recommendations to supercharge gene prioritisation + disease understanding