<span class="var-sub_title">OpeNNdd: Open Neural Networks for Drug Discovery: Creating Free and Easy Methods for Designing Medicine</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

OpeNNdd: Open Neural Networks for Drug Discovery: Creating Free and Easy Methods for Designing Medicine

Authors: Bryce Kroencke (American River College), Shawn Shacterman (University of California, Berkeley), Nicholas Pavini (American River College), Benjamin Samudio (American River College, Sierra College), Silvia Crivelli (Lawrence Berkeley National Laboratory)

Abstract: Bringing new medicines to patients can be prohibitively expensive in terms of time, cost, and resources. This leaves many diseases without therapeutic interventions. In addition, new and reemerging diseases are increasing in prevalence across the globe at an alarming rate. The speed and scale of medicine discovery must be increased to effectively meet this challenge. OpeNNdd is a neural network platform bringing together people, machine learning, and supercomputing to solve the challenge of creating medicines. We have developed a novel neural network which quickly and accurately models candidate medicines interacting with a disease target, a metric to delineate its domain of applicability, and a process that communicates neural network results to participants in a readily interpretable way. OpeNNdd leverages the scale of supercomputing, the power and speed of neural networks, and the creativity of people across the globe in an open and collaborative way to protect and improve global health.

Best Poster Finalist (BP): no

Poster: pdf
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