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Exascale Compound Activity Prediction Engine

Objectif

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.

Coordinateur

INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM
Contribution nette de l'UE
€ 873 146,25
Adresse
KAPELDREEF 75
3001 Leuven
Belgique

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Région
Vlaams Gewest Prov. Vlaams-Brabant Arr. Leuven
Type d’activité
Research Organisations
Liens
Coût total
€ 873 146,25

Participants (9)