Using artificial neural networks to predict cell-penetrating compounds

Abstract

Introduction: Membrane-cell penetration is a key property for drug candidates, particularly those related to CNS and gastrointestinal diseases. The ability to know whether a drug or compound has the ability to perform this complex characteristic in advance would save time and money for pharmaceutical companies. One robust and fast solution is to use artificial neural networks (ANNs) to predict the cell penetration of the compound candidates.

Areas covered: The authors review the application of ANN methods for ANN modeling in the discovery of cell-penetrating drugs. The article looks at three main systems including the BBB, gastrointestinal absorption and permeation in addition to discussing a new approach for cell-penetrating peptide discovery. This review provides the reader with an overview of the ANN methods and applications for the broader audience interested in prediction of cell penetration of drugs.

Expert opinion: ANNs can be successfully applied to the prediction of cell-penetrating drugs. Researchers have a broad field of applications for the use of quantitative structure–activity relationship neural networks in drug discovery and development, and can use these areas to further investigate this important pharmaceutical topic.

Keywords::

• artificial neural network
• BBB penetration
• cell membrane permeability
• cell-penetrating peptides
• gastrointestinal permeation
• QSAR

Notes

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