A Stochastic Spiking Neural Network for Virtual Screening
| Autor: | Josep L. Rosselló, F. Galan-Prado, Antoni Morro, Antoni Oliver, P. J. Ballester, Miquel L. Alomar, Vincent Canals |
|---|---|
| Přispěvatelé: | Department of Physics electronics engineering group, University of the Balearic Islands (UIB), Electr. Techn. Group, Universitat de les Illes Balears (UIB), Universidad de las Islas Baleares (UIB), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer Networks and Communications
Computer science Nearest neighbor search [SDV]Life Sciences [q-bio] 02 engineering and technology Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Relevance (information retrieval) Spiking neural network Virtual screening business.industry Volume (computing) Computer Science Applications Computer engineering Logic gate Key (cryptography) 020201 artificial intelligence & image processing State (computer science) Artificial intelligence business computer 030217 neurology & neurosurgery Software |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2018, 29 (4), pp.1371-1375. ⟨10.1109/TNNLS.2017.2657601⟩ IEEE Transactions on Neural Networks and Learning Systems, 2018, 29 (4), pp.1371-1375. ⟨10.1109/TNNLS.2017.2657601⟩ |
| ISSN: | 2162-237X |
| Popis: | International audience; Virtual screening (VS) has become a key computational tool in early drug design and screening performance is of high relevance due to the large volume of data that must be processed to identify molecules with the sought activity-related pattern. At the same time, the hardware implementations of spiking neural networks (SNNs) arise as an emerging computing technique that can be applied to parallelize processes that normally present a high cost in terms of computing time and power. Consequently, SNN represents an attractive alternative to perform time-consuming processing tasks, such as VS. In this brief, we present a smart stochastic spiking neural architecture that implements the ultrafast shape recognition (USR) algorithm achieving two order of magnitude of speed improvement with respect to USR software implementations. The neural system is implemented in hardware using field-programmable gate arrays allowing a highly parallelized USR implementation. The results show that, due to the high parallelization of the system, millions of compounds can be checked in reasonable times. From these results, we can state that the proposed architecture arises as a feasible methodology to efficiently enhance time-consuming data-mining processes such as 3-D molecular similarity search. |
| Databáze: | OpenAIRE |
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