AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis.
Mol Pharm
; 21(2): 864-872, 2024 Feb 05.
Article
em En
| MEDLINE
| ID: mdl-38134445
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-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.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fosfolipídeos
/
Lipidoses
Limite:
Humans
Idioma:
En
Revista:
Mol Pharm
Assunto da revista:
BIOLOGIA MOLECULAR
/
FARMACIA
/
FARMACOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Itália