Random Forest Model Prediction of Compound Oral Exposure in the Mouse

Haseeb Mughal, Han Wang, Matthew Zimmerman, Marc D. Paradis, Joel S. Freundlich

Research output: Contribution to journalArticlepeer-review


An early hurdle in the optimization of small-molecule chemical probes and drug discovery entities is the attainment of sufficient exposure in the mouse via oral administration of the compound. While computational approaches have attempted to predict molecular properties related to the mouse pharmacokinetic (PK) profile, we present herein a machine learning approach to specifically predict the oral exposure of a compound as measured in the mouse snapshot PK assay. A random forest workflow was found to produce the best cross-validation and external test set statistics after processing of the input data set and optimization of model features. The modeling approach should be useful to the chemical biology and drug discovery communities to predict this key molecular property and afford chemical entities of translational significance.

Original languageEnglish (US)
Pages (from-to)338-343
Number of pages6
JournalACS Pharmacology and Translational Science
Issue number1
StatePublished - Feb 12 2021

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Pharmacology (medical)


  • area under the curve
  • machine learning model
  • mouse pharmacokinetics
  • oral exposure
  • snapshot PK


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