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2017 GTC San Jose

S7713 - The Atoms of Artificial Intelligence in Drug Discovery

Session Speakers
Session Description

The history of medicine discovery and development has been predicated on a model of efficiency?fail early and minimize costs to proof of concept. This has proven to be an expensive model. That model has generated a wealth of data and information in the healthcare sector on failed compounds and approaches as well as on the few that have become medicines. Advances in science and technology are revealing biology at an ever increasing rate, the translation of which to benefit human health may be accelerated by integration of capabilities that have emerged in adjacent science, engineering, and technology disciplines. This translation can be significantly informed by the consolidation and use of the failed molecules that litter the medicine discovery and development landscape. There is an obligation to society to use the wealth of precompetitive and non-competitive information gained in the failures of the efficiency model to inform a modernized medicine discovery model based on the effectiveness model. A collaboration, ATOM, has been formed to explore how high performance computing, biology, and drug discovery come together to create new drug discovery systems based on a fast compute.


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Talk
AI in Healthcare Summit Healthcare and Life Sciences Medical Imaging
Healthcare & Life Sciences
25 minutes
Session Schedule