دورية أكاديمية
AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis.
العنوان: | AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis. |
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المؤلفون: | Lomuscio MC; CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy., Abate C; CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy.; Department of Pharmacy-Pharmaceutical Sciences, University of the Studies of Bari 'Aldo Moro', Via E.Orabona 4, 70125 Bari, Italy., Alberga D; CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy., Laghezza A; Department of Pharmacy-Pharmaceutical Sciences, University of the Studies of Bari 'Aldo Moro', Via E.Orabona 4, 70125 Bari, Italy., Corriero N; CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy., Colabufo NA; Department of Pharmacy-Pharmaceutical Sciences, University of the Studies of Bari 'Aldo Moro', Via E.Orabona 4, 70125 Bari, Italy., Saviano M; CNR─Institute of Crystallography, Via Vivaldi 43, 81100 Caserta, Italy., Delre P; CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy., Mangiatordi GF; CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy. |
المصدر: | Molecular pharmaceutics [Mol Pharm] 2024 Feb 05; Vol. 21 (2), pp. 864-872. Date of Electronic Publication: 2023 Dec 22. |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101197791 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1543-8392 (Electronic) Linking ISSN: 15438384 NLM ISO Abbreviation: Mol Pharm Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: Washington, DC : American Chemical Society, c2004- |
مواضيع طبية MeSH: | Phospholipids* , Lipidoses*/chemically induced, Humans ; Hep G2 Cells ; Lysosomes ; Machine Learning ; Antiviral Agents/adverse effects |
مستخلص: | 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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فهرسة مساهمة: | Keywords: SARS-CoV-2; ligand-based classifiers; machine learning; phospholipidosis |
المشرفين على المادة: | 0 (Phospholipids) 0 (Antiviral Agents) |
تواريخ الأحداث: | Date Created: 20231222 Date Completed: 20240206 Latest Revision: 20240211 |
رمز التحديث: | 20240211 |
مُعرف محوري في PubMed: | PMC10853961 |
DOI: | 10.1021/acs.molpharmaceut.3c00964 |
PMID: | 38134445 |
قاعدة البيانات: | MEDLINE |
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