Predicting Phospholipidosis Using Machine Learning

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.