Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives

Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K...

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Bibliographic Details
Main Authors: Natalja Fjodorova, Marjana Novič, Katja Venko, Bakhtiyor Rasulev, Melek Türker Saçan, Gulcin Tugcu, Safiye Sağ Erdem, Alla P. Toropova, Andrey A. Toropov
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:International Journal of Molecular Sciences
Subjects:
fullerene-based nanomaterials
fullerene derivatives
artificial neural network
aquatic toxicity
protein–ligand binding activity
binding affinity
Online Access:https://www.mdpi.com/1422-0067/24/18/14160

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https://www.mdpi.com/1422-0067/24/18/14160

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