AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis

Drug-induced phospholipidosis (PLD) involves the accumulation
of
phospholipids in cells of multiple tissues, particularly within lysosomes,
and it is associated with prolonged exposure to druglike compounds,
predominantly cationic amphiphilic drugs (CADs). PLD affects a significant
portion of drugs currently in development and has recently been proven
to be responsible for confounding antiviral data during drug repurposing
for SARS-CoV-2. In these scenarios, it has become crucial to identify
potential safe drug candidates in advance and distinguish them from
those that may lead to false in vitro antiviral activity. In this
work, we developed a series of machine learning classifiers with the
aim of predicting the PLD-inducing potential of drug candidates. The
models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The
most effective model, obtained using the balanced random forest algorithm,
achieved high performance, including an AUC value computed in validation
as high as 0.90. The model was made freely available through a user-friendly
web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested
in conducting an early evaluation of PLD inducer potential.