Deep Learning in Drug Design

Description

Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, language and vision benchmarks. Consequently it became the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. Using Deep Learning we won the NIH Tox21 challenge organized by the US agencies NIH, EPA, and FDA, which was an unprecedented multi-million-dollar effort to test toxicity prediction methods. In collaboration with pharma companies Deep Learning has identified unknown side effects of drug candidates when given their chemical structure and learned on data from bioassays. We extended this approach to high content imaging, where we detect biological effects given images of cell lines to which a compound was added. We deploy Deep Neural Networks to toxicity and target prediction in collaboration with Janssen, Merck, Novartis, AstraZeneca, GSK, Bayer together with hardware-related companies like Intel, HP, NVIDIA and others.

Period	28 Jul 2017
Event title	Computational Approaches in Precision Medicine
Event type	Conference
Location	AustriaShow on map