Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development.
BACKGROUND
The cost of developing new drugs is estimated at approximately $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.


METHODS
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.


RESULTS
This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms.


CONCLUSIONS
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.
Klon
Anthony E
AE
Ansaris, Computational Chemistry, Four Valley Square, 512 East Township Line Road, Blue Bell, PA 19422, USA. aklon@ansarisbio.com
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism*; D055808:Drug Discovery / Q000379:methods*; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels / Q000378:metabolism*; D005544:Forecasting; D006801:Humans; D007527:Isoenzymes / Q000378:metabolism; D011485:Protein Binding / Q000502:physiology; D020033:Protein Isoforms / Q000378:metabolism
D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; D007527:Isoenzymes; C000606913:KCNH2 protein, human; D020033:Protein Isoforms; D003577:Cytochrome P-450 Enzyme System