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Exascale Compound Activity Prediction Engine

Objetivo

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.

Régimen de financiación

RIA - Research and Innovation action

Coordinador

INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM
Aportación neta de la UEn
€ 873 146,25
Dirección
KAPELDREEF 75
3001 Leuven
Bélgica

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Región
Vlaams Gewest Prov. Vlaams-Brabant Arr. Leuven
Tipo de actividad
Research Organisations
Enlaces
Coste total
€ 873 146,25

Participantes (9)