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

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The 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.
Publisher
IEEE COMPUTER SOC
Issue Date
2018-03
Language
English
Article Type
Article; Proceedings Paper
Keywords

DRUG DISCOVERY; GASTROINTESTINAL ABSORPTION; ORAL BIOAVAILABILITY; CACO-2 MONOLAYERS; NEURAL-NETWORKS; ADME EVALUATION; IN-SILICO; PERMEABILITY; MOLECULES; TRANSPORT

Citation

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, v.15, no.2, pp.432 - 440

ISSN
1545-5963
DOI
10.1109/TCBB.2016.2535233
URI
http://hdl.handle.net/10203/241539
Appears in Collection
BiS-Journal Papers(저널논문)
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