CNN in drug design : recent developments

2015
book section
conference proceedings
3
dc.abstract.enWe describe a method for construction of specific types of Neural Networks composed of structures directly linked to the structure of the molecule under consideration. Each molecule can be represented by a unique neural connectivity problem (graph) which can be programmed onto a Cellular Neural Network. The idea was to translate chemical structures like small organic molecules or peptides into a self learning environment which is CNN based. In the case of small molecules, each cell of the CNN stands for one atom of the molecule under consideration. But in contrast to the standard CNN architecture where each cell is connected to the neighboring cells, only those cells of the feature net are connected for which there also exists a chemical bond in the molecule under consideration. This implies that the feature net topology varies from molecule to molecule. In the case of peptides, the amino acids that form the building blocks of the peptide are reflected by the CNN cells wherein the amino acid sequence defines the network topology. Unlike the standard CNN used for image processing, there are no input values like the input image that are fed into the feature net. Instead, all information about the input molecule is supplied to the feature net by means of the topology. The output of several feature nets is fed into a supervisor neural network which computes the final output value. The combination of several feature nets and a supervisor networks constitutes the Molecular Graph Network (MGN). The designed networks are used for selection of molecules representing wanted properties such as activity against specific diseases, interactions with other compounds, toxicity etc. and possibly being candidates to be tested further as new drugs.pl
dc.affiliationWydział Fizyki, Astronomii i Informatyki Stosowanej : Zakład Technologii Informatycznychpl
dc.conferenceInternational Symposium on Circuits and Systems (ISCAS 2015)pl
dc.conference.cityLizbona
dc.conference.countryPortugalia
dc.conference.datefinish2015-05-27
dc.conference.datestart2015-05-24
dc.conference.indexscopustrue
dc.conference.indexwostrue
dc.contributor.authorWichard, Joerg D.pl
dc.contributor.authorOgorzałek, Maciej - 102456 pl
dc.contributor.authorMerkwirth, Christianpl
dc.date.accessioned2015-12-29T13:42:21Z
dc.date.available2015-12-29T13:42:21Z
dc.date.issued2015pl
dc.description.conftypeinternationalpl
dc.description.physical405-408pl
dc.description.publication0,2pl
dc.identifier.doi10.1109/ISCAS.2015.7168656pl
dc.identifier.isbn978-1-4799-8392-6pl
dc.identifier.urihttp://ruj.uj.edu.pl/xmlui/handle/item/18738
dc.languageengpl
dc.language.containerengpl
dc.pubinfoPiscataway : IEEEpl
dc.rightsDodaję tylko opis bibliograficzny*
dc.rights.licencebez licencji
dc.rights.uri*
dc.subtypeConferenceProceedingspl
dc.titleCNN in drug design : recent developmentspl
dc.title.container2015 IEEE International Symposium on Circuits and Systems (ISCAS 2015) : Lisbon, Portugal, 24-27 May 2015pl
dc.typeBookSectionpl
dspace.entity.typePublication
dc.abstract.enpl
We describe a method for construction of specific types of Neural Networks composed of structures directly linked to the structure of the molecule under consideration. Each molecule can be represented by a unique neural connectivity problem (graph) which can be programmed onto a Cellular Neural Network. The idea was to translate chemical structures like small organic molecules or peptides into a self learning environment which is CNN based. In the case of small molecules, each cell of the CNN stands for one atom of the molecule under consideration. But in contrast to the standard CNN architecture where each cell is connected to the neighboring cells, only those cells of the feature net are connected for which there also exists a chemical bond in the molecule under consideration. This implies that the feature net topology varies from molecule to molecule. In the case of peptides, the amino acids that form the building blocks of the peptide are reflected by the CNN cells wherein the amino acid sequence defines the network topology. Unlike the standard CNN used for image processing, there are no input values like the input image that are fed into the feature net. Instead, all information about the input molecule is supplied to the feature net by means of the topology. The output of several feature nets is fed into a supervisor neural network which computes the final output value. The combination of several feature nets and a supervisor networks constitutes the Molecular Graph Network (MGN). The designed networks are used for selection of molecules representing wanted properties such as activity against specific diseases, interactions with other compounds, toxicity etc. and possibly being candidates to be tested further as new drugs.
dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Zakład Technologii Informatycznych
dc.conferencepl
International Symposium on Circuits and Systems (ISCAS 2015)
dc.conference.city
Lizbona
dc.conference.country
Portugalia
dc.conference.datefinish
2015-05-27
dc.conference.datestart
2015-05-24
dc.conference.indexscopus
true
dc.conference.indexwos
true
dc.contributor.authorpl
Wichard, Joerg D.
dc.contributor.authorpl
Ogorzałek, Maciej - 102456
dc.contributor.authorpl
Merkwirth, Christian
dc.date.accessioned
2015-12-29T13:42:21Z
dc.date.available
2015-12-29T13:42:21Z
dc.date.issuedpl
2015
dc.description.conftypepl
international
dc.description.physicalpl
405-408
dc.description.publicationpl
0,2
dc.identifier.doipl
10.1109/ISCAS.2015.7168656
dc.identifier.isbnpl
978-1-4799-8392-6
dc.identifier.uri
http://ruj.uj.edu.pl/xmlui/handle/item/18738
dc.languagepl
eng
dc.language.containerpl
eng
dc.pubinfopl
Piscataway : IEEE
dc.rights*
Dodaję tylko opis bibliograficzny
dc.rights.licence
bez licencji
dc.rights.uri*
dc.subtypepl
ConferenceProceedings
dc.titlepl
CNN in drug design : recent developments
dc.title.containerpl
2015 IEEE International Symposium on Circuits and Systems (ISCAS 2015) : Lisbon, Portugal, 24-27 May 2015
dc.typepl
BookSection
dspace.entity.type
Publication

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