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.

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

“…They segmented small molecules into atoms and bonds to build a digraph by sequencing those atoms and linking them using their corresponding bonds, and then put the contracted graph into an RNN model. In 2015, Shin et al published their model developed using DL method to predict the absorption potential of small molecules (66). In vitro permeability data of 663 small molecules from the human colorectal carcinoma cell line (Caco-2) were used as training data and 209 molecular descriptors were calculated using CDK toolkits based on their 2D structures (http://www.rguha.net/code/java/cdkdesc.html).…”

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

“…They have widely been applied to fields particularly computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, and various games (Collobert and Weston, 2008;Bengio, 2009;Dahl et al, 2012;Hinton et al, 2012;LeCun et al, 2015;Defferrard et al, 2016;Mamoshina et al, 2016), where they have produced results comparable to or in some cases superior to human experts. In recent years, deep learning has also been applied to drug discovery, and it has demonstrated its potentials (Lusci et al, 2013;Ma et al, 2015;Xu et al, 2015;Aliper et al, 2016;Mayr et al, 2016;Pereira et al, 2016;Subramanian et al, 2016;Kadurin et al, 2017;Ragoza et al, 2017;Ramsundar et al, 2017;Xu et al, 2017;Ghasemi et al, 2018;Harel and Radinsky, 2018;Hu et al, 2018;Popova et al, 2018;Preuer et al, 2018;Russo et al, 2018;Segler et al, 2018;Shin et al, 2018;Cai et al, 2019;Wang et al, 2019a;Yang et al, 2019). However, there are still some issues that limit the application of deep learning in drug discovery.…”

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

“…In addition, there are sophisticated studies using deep learning techniques to resolve current limitations. Shin et al [125] collected Caco-2 cell permeability data from literatures and constructed a deep neural network (DNN) model to reduce feature selection bias. Wenzel et al [126] proposed a multitask DNN model to relieve the data deficiency with ChEMBL dataset which contains Caco-2 cell permeability and microsomal clearances.…”