ExCAPE
Term: 9/2015 - 8/2018 (36 months)
Project full title:
Exascale Compound Activity Prediction Engine
Abstract:
Scalable machine learning of complex models on extreme data will be an important industrial application of exascale computers. In this project, we take the example of predicting compound bioactivity for the pharmaceutical industry, an important sector for Europe for employment, income, and solving the problems of an ageing society. Small scale approaches to machine learning have already been trialed and show great promise to reduce empirical testing costs by acting as a virtual screen to filter out tests unlikely to work. However, it is not yet possible to use all available data to make the best possible models, as algorithms (and their implementations) capable of learning the best models do not scale to such sizes and heterogeneity of input data. There are also further challenges including imbalanced data, confidence estimation, data standards model quality and feature diversity. The ExCAPE project aims to solve these problems by producing state of the art scalable algorithms and implementations thereof suitable for running on future Exascale machines. These approaches will scale programs for complex pharmaceutical workloads to input data sets at industry scale. The programs will be targeted at exascale platforms by using a mix of HPC programming techniques, advanced platform simulation for tuning and and suitable accelerators.
INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM VZW, Belgium
CTO / INTELLAB, Belgium
JANSSEN CILAG SA, Spain
VYSOKA SKOLA BANSKA - TECHNICKA UNIVERZITA OSTRAVA, Czech Republic
ASTRAZENECA AB, Sweden
AALTO-KORKEAKOULUSAATIO, Finland
INTEL CORPORATION S.A., Belgium
IDEACONSULT LIMITED LIABILITY COMPANY, Bulgaria
ROYAL HOLLOWAY AND BEDFORD NEW COLLEGE, United Kingdom Project funded by the European Commission, H2020-FETHPC-2014