title: Predicting phospholipidosis using machine learning.
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.
authors.affiliation: Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
authors.forename: Robert
authors.initials: R
authors.lastname: Lowe
authors.forename: Robert C
authors.initials: RC
authors.lastname: Glen
authors.forename: John B O
authors.initials: JB
authors.lastname: Mitchell
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D016208:Databases, Factual; D055808:Drug Discovery; D006801:Humans; D008064:Lipidoses / Q000139:chemically induced* / Q000378:metabolism*; D008954:Models, Biological; D010743:Phospholipids / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D010743:Phospholipids
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