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Wednesday, April 22 • 3:30pm - 4:10pm
Molecules are Graphs! Lowering the Costs of Drug Discovery with Neo4j

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Molecules are graphs! When you change part of this graph, swap one part out for another, add something in here, remove a little there you change how that molecule behaves and interacts with your body. This talk models alterations of molecular graphs as a network and applies it to drug discovery!

**Molecules are graphs!** The nodes are atoms and the edges are bonds. What happens when we take these graphs and their properties and put them in a graph database?

**Drug discovery is expensive**. The cost of producing a new drug is estimated to cost $2.6 Bn with a significant chunk of that cost coming from research and development of the drug molecule. Reducing down the cost of developing new therapeutics is key to helping patients. If researchers can make smarter decisions earlier in R&D the timelines and cost of bringing new drugs to patients will be reduced.

**Matched molecular pair** analysis (MMPA) is a method used in chemoinformatics that compares the properties of two molecules that differ only by a single chemical transformation (or graph alteration). An example of this would be if you were looking at two molecules that only differed by the substitution of a hydrogen atom for a fluorine atom. These two molecules whilst almost identical could have vastly different chemical properties. These **pairs** of compounds are known as **matched molecular pairs** (MMP), and any change in the properties of these molecules can be modelled on an edge linking them together.

This talk will explore how combining biological assay data with these chemical transformations in a **matched molecular pair knowledge graph (MMPKG)** using Neo4J allows for powerful exploration of chemical data. Through the use of Neo4j browser, cypher, and graph algorithms library new insights can be gathered to help answer the question on most medicinal chemists lips... _""which molecule should I make next?""_ and exactly how the MMPKG can be used and applied to real drug discovery problems to help drive this decision making process."

avatar for Matthew Sellwood

Matthew Sellwood

Product Manager, IQVia
Matthew is currently a Product Manager at IQVIA, and has worked across the life sciences and healthcare industry. He earned his Masters of Chemistry and his PhD at the University of Sheffield in the UK. In his thesis work he researched the discovery of novel therapeutics for ALS (Lou... Read More →

Wednesday April 22, 2020 3:30pm - 4:10pm EDT
Room 4