Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein-ligand binding in order to identify the key binding characteristics of FDs. Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction. The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles. All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.
Keywords: Anti-diabetic targets; Fullerene derivatives; Fullerene-based nanoparticles; Neural network models; Protein–ligand binding.
© 2022 The Authors.