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dc.contributor.authorKarim, A
dc.contributor.authorSingh, J
dc.contributor.authorMishra, A
dc.contributor.authorDehzangi, A
dc.contributor.authorNewton, MAH
dc.contributor.authorSattar, A
dc.date.accessioned2020-04-07T01:04:11Z
dc.date.available2020-04-07T01:04:11Z
dc.date.issued2019
dc.identifier.isbn9783030306380
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-030-30639-7_12
dc.identifier.urihttp://hdl.handle.net/10072/393009
dc.description.abstractPrediction of toxicity levels of chemical compounds is an important issue in Quantitative Structure-Activity Relationship (QSAR) modeling. Although toxicity prediction has achieved significant progress in recent times through deep learning, prediction accuracy levels obtained by even very recent methods are not yet very high. We propose a multimodal deep learning method using multiple heterogeneous neural network types and data representations. We represent chemical compounds by strings, images, and numerical features. We train fully connected, convolutional, and recurrent neural networks and their ensembles. Each data representation or neural network type has its own strengths and weaknesses. Our motivation is to obtain a collective performance that could go beyond individual performance of each data representation or each neural network type. On a standard toxicity benchmark, our proposed method obtains significantly better accuracy levels than that by the state-of-the-art toxicity prediction methods.
dc.description.peerreviewedYes
dc.publisherSpringer
dc.relation.ispartofconferencename16th Pacific Rim Knowledge Acquisition Workshop (PKAW 2019)
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofdatefrom2019-08-26
dc.relation.ispartofdateto2019-08-27
dc.relation.ispartoflocationCuvu, Fiji
dc.relation.ispartofpagefrom142
dc.relation.ispartofpageto152
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.ispartofvolume11669
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleToxicity Prediction by Multimodal Deep Learning
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationKarim, A; Singh, J; Mishra, A; Dehzangi, A; Newton, MAH; Sattar, A, Toxicity Prediction by Multimodal Deep Learning, Knowledge Management and Acquisition for Intelligent Systems, 2019, 11669, pp. 142-152
dc.date.updated2020-04-07T01:01:21Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© Springer Nature Switzerland AG 2019. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorNewton, MAHakim A.
gro.griffith.authorKarim, Abdul
gro.griffith.authorSingh, Jaspreet


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