title: Predicting the Absorption Potential of Chemical Compounds Through a Deep Learning Approach.
abstract: 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 DNN-based binary Caco-2 permeability classifier. Our model was constructed based on 663 chemical compounds with in-vitro Caco-2 apparent permeability data. Two hundred nine 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.
authors.forename: Moonshik
authors.initials: M
authors.lastname: Shin
authors.forename: Donjin
authors.initials: D
authors.lastname: Jang
authors.forename: Hojung
authors.initials: H
authors.lastname: Nam
authors.forename: Kwang Hyung
authors.initials: KH
authors.lastname: Lee
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
mesh_terms: D065966:Absorption, Physicochemical / Q000187:drug effects*; D018938:Caco-2 Cells; D002463:Cell Membrane Permeability / Q000187:drug effects; D019295:Computational Biology; D003198:Computer Simulation; D000077321:Deep Learning; D004353:Drug Evaluation, Preclinical; D006801:Humans; D015233:Models, Statistical; D004364:Pharmaceutical Preparations / Q000378:metabolism*
chemical_list: D004364:Pharmaceutical Preparations