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

ABSTRACT: 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-qua

SUBMITTER: Lomuscio MC 

PROVIDER: S-EPMC10853961 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature