Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development

Abstract

Importance of the field: The cost of developing new drugs is estimated at ∼ $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market.

Areas covered in this review: The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development.

What the reader will gain: This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms.

Take home message: A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.

Keywords::

• CYP
• hERG
• machine learning
• metabolism
• Torsade de pointes
• toxicity

Notes

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