Predicting Phospholipidosis Using Machine Learning

Robert Lowe, Robert C. Glen, John Blayney Owen Mitchell

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the importance of computational approaches to the problem has been well documented. Previous work on predictive methods for phospholipidosis showed that state of the art machine learning methods produced the best results. Here we extend this work by looking at a larger data set mined from the literature. We find that circular fingerprints lead to better models than either E-Dragon descriptors or a combination of the two. We also observe very similar performance in general between Random Forest and Support Vector Machine models.

Original languageEnglish
Pages (from-to)1708-1714
Number of pages7
JournalMolecular Pharmaceutics
Volume7
Issue number5
DOIs
Publication statusPublished - 2010

Keywords

  • Phospholipidosis
  • machine learning
  • Random Forest
  • Support Vector Machine
  • in silico
  • prediction
  • DRUG-INDUCED PHOSPHOLIPIDOSIS
  • SUPPORT VECTOR MACHINES
  • CLASSIFICATION
  • DESCRIPTORS
  • INDUCTION
  • CHEMISTRY
  • MECHANISM
  • DESIGN
  • SYSTEM

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