Predicting the Absorption Potential of Chemical Compounds through a Deep Learning Approach

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dc.contributor.authorShin, Moonshikko
dc.contributor.authorJang, Dongjinko
dc.contributor.authorNam, Hojungko
dc.contributor.authorLee, Kwang-Hyungko
dc.contributor.authorLee, Doheonko
dc.date.accessioned2018-05-23T06:31:08Z-
dc.date.available2018-05-23T06:31:08Z-
dc.date.created2016-12-27-
dc.date.created2016-12-27-
dc.date.issued2018-03-
dc.identifier.citationIEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, v.15, no.2, pp.432 - 440-
dc.identifier.issn1545-5963-
dc.identifier.urihttp://hdl.handle.net/10203/241539-
dc.description.abstractThe human colorectal carcinoma cell line (Caco-2) is a commonly used in-vitro test that predicts the absorption potential of orally administered drugs. In-silico prediction methods, based on the Caco-2 assay data, may increase the effectiveness of the high-throughput screening of new drug candidates. However, previously developed in-silico models that predict the Caco-2 cellular permeability of chemical compounds use handcrafted features that may be dataset-specific and induce over-fitting problems. Deep Neural Network (DNN) generates high-level features based on non-linear transformations for raw features, which provides high discriminant power and, therefore, creates a good generalized model. We present a DNNbased binary Caco-2 permeability classifier. Our model was constructed based on 663 chemical compounds with in-vitro Caco-2 apparent permeability data. 209 molecular descriptors are used for generating the high-level features during DNN model generation. Dropout regularization is applied to solve the over-fitting problem and the non-linear activation. The Rectified Linear Unit (ReLU) is adopted to reduce the vanishing gradient problem. The results demonstrate that the high-level features generated by the DNN are more robust than handcrafted features for predicting the cellular permeability of structurally diverse chemical compounds in Caco-2 cell lines.-
dc.languageEnglish-
dc.publisherIEEE COMPUTER SOC-
dc.subjectDRUG DISCOVERY-
dc.subjectGASTROINTESTINAL ABSORPTION-
dc.subjectORAL BIOAVAILABILITY-
dc.subjectCACO-2 MONOLAYERS-
dc.subjectNEURAL-NETWORKS-
dc.subjectADME EVALUATION-
dc.subjectIN-SILICO-
dc.subjectPERMEABILITY-
dc.subjectMOLECULES-
dc.subjectTRANSPORT-
dc.titlePredicting the Absorption Potential of Chemical Compounds through a Deep Learning Approach-
dc.typeArticle-
dc.identifier.wosid000428936900010-
dc.identifier.scopusid2-s2.0-85044928925-
dc.type.rimsART-
dc.citation.volume15-
dc.citation.issue2-
dc.citation.beginningpage432-
dc.citation.endingpage440-
dc.citation.publicationnameIEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS-
dc.identifier.doi10.1109/TCBB.2016.2535233-
dc.contributor.localauthorLee, Kwang-Hyung-
dc.contributor.localauthorLee, Doheon-
dc.contributor.nonIdAuthorNam, Hojung-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle; Proceedings Paper-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthorneural nets-
dc.subject.keywordAuthorCaco-2 permeability-
dc.subject.keywordAuthorabsorption prediction-
dc.subject.keywordPlusDRUG DISCOVERY-
dc.subject.keywordPlusGASTROINTESTINAL ABSORPTION-
dc.subject.keywordPlusORAL BIOAVAILABILITY-
dc.subject.keywordPlusCACO-2 MONOLAYERS-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusADME EVALUATION-
dc.subject.keywordPlusIN-SILICO-
dc.subject.keywordPlusPERMEABILITY-
dc.subject.keywordPlusMOLECULES-
dc.subject.keywordPlusTRANSPORT-
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