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dc.contributor.author | Kuzminykh D. | |
dc.contributor.author | Polykovskiy D. | |
dc.contributor.author | Kadurin A. | |
dc.contributor.author | Zhebrak A. | |
dc.contributor.author | Baskov I. | |
dc.contributor.author | Nikolenko S. | |
dc.contributor.author | Shayakhmetov R. | |
dc.contributor.author | Zhavoronkov A. | |
dc.date.accessioned | 2019-01-22T20:46:15Z | |
dc.date.available | 2019-01-22T20:46:15Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 1543-8384 | |
dc.identifier.uri | https://dspace.kpfu.ru/xmlui/handle/net/148683 | |
dc.description.abstract | © 2018 American Chemical Society. Convolutional neural networks (CNN) have been successfully used to handle three-dimensional data and are a natural match for data with spatial structure such as 3D molecular structures. However, a direct 3D representation of a molecule with atoms localized at voxels is too sparse, which leads to poor performance of the CNNs. In this work, we present a novel approach where atoms are extended to fill other nearby voxels with a transformation based on the wave transform. Experimenting on 4.5 million molecules from the Zinc database, we show that our proposed representation leads to better performance of CNN-based autoencoders than either the voxel-based representation or the previously used Gaussian blur of atoms and then successfully apply the new representation to classification tasks such as MACCS fingerprint prediction. | |
dc.relation.ispartofseries | Molecular Pharmaceutics | |
dc.subject | 3D convolutional neural networks | |
dc.subject | autoencoders | |
dc.subject | wave transform | |
dc.subject | wavelets | |
dc.title | 3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks | |
dc.type | Article | |
dc.relation.ispartofseries-issue | 10 | |
dc.relation.ispartofseries-volume | 15 | |
dc.collection | Публикации сотрудников КФУ | |
dc.relation.startpage | 4378 | |
dc.source.id | SCOPUS15438384-2018-15-10-SID85052122681 |