NIPS 2018 Expo Talk

Dec. 2, 2018

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Artificial Intelligence for target identification in drug discovery

Sponsor: BenevolentAI

Celine Lature (BenevolentAI)


A key challenge in the drug discovery process is the identification of potential therapeutic targets for a given disease. This process requires selecting both a target and an entity, often a compound, for modulating the target’s activity to validate its association with a disease. An additional complication is that heretofore unknown targets may be more challenging to identify but offer increased opportunities for the development of novel drugs. At BenevolentAI, we use machine learning throughout the entire process. Relation extraction models drive our unstructured pipeline. In conjunction with structured data and our own experimental results, this processing pipeline ingests information from scientific publications, abstracts, patents. Next, this biological knowledge graph is leveraged to form predictions using relational inference algorithms, including matrix factorization and graph convolutional models. These predictions are aggregated together with machine learning models built on genomics data and information mined from text. Druggability, tissue specificity, and other metadata are then added to surface the most promising targets to test in the lab. This talk will describe our process and highlight some of our early successes