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Drug Discovery

Introduction

Drug development is often expansive and time-consuming. Vast amounts of chemical compounds need to be tested under specific conditions in order to find the most effective ones for certain diseases. Modern drug development aims to optimize this resource-intensive process by leveraging machine learning techniques combined with new and effective ways of data representation.

Machine learning is also used in early experimental stages to predict molecular properties and significantly reduce the resources and time needed for drug discovery. Relational databases are not equipped to handle such vast amounts of unstrutured biological data, which is why graph databases and graph algorithms have found a permanent foothold in the industry.

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Applying graphs

Knowledge graph prediction of unknown adverse drug reactions

Unknown adverse reactions to drugs present a significant health risk and limit accurate judgment. Machine learning has the potential to predict unknown adverse reactions from current knowledge without having to rely on new resources intensive studies and experiments. Representing relationships in the form of a graph and performing feature extraction can result in much clearer and more accurate predictions. Traditional AI and machine learning techniques are not meant to detect and traverse complex relationships on such scale which is where graph algorithms come in.

Where to next?

This text is a summary of one area that fits perfectly with the application of graphs. Therefore, we would like to have you with us when implementing some of these solutions. Share opinions, experiences and problems you encounter when working with Memgraph on our Discord server. We are here for you and we will help you along the way.