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

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dc.contributor.authorERDEM, SAFİYE
dc.contributor.authorsFjodorova N., Novič M., Venko K., Rasulev B., Türker Saçan M., Tugcu G., Sağ Erdem S., Toropova A. P., Toropov A. A.
dc.date.accessioned2023-10-19T06:13:52Z
dc.date.available2023-10-19T06:13:52Z
dc.date.issued2023-09-01
dc.description.abstractFullerene 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, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)—as a known target of toxins in fathead minnows and Daphnia magna, causing the inhibition of AChE—was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure–activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.
dc.identifier.citationFjodorova N., Novič M., Venko K., Rasulev B., Türker Saçan M., Tugcu G., Sağ Erdem S., Toropova A. P., Toropov A. A., "Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives", International Journal of Molecular Sciences, cilt.24, sa.18, 2023
dc.identifier.doi10.3390/ijms241814160
dc.identifier.issn1661-6596
dc.identifier.issue18
dc.identifier.urihttps://avesis.marmara.edu.tr/api/publication/8248872d-ff69-4e42-970d-65d9fea27b57/file
dc.identifier.urihttps://hdl.handle.net/11424/294405
dc.identifier.volume24
dc.language.isoeng
dc.relation.ispartofInternational Journal of Molecular Sciences
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBilgisayar Bilimleri
dc.subjectKimya Mühendisliği ve Teknolojisi
dc.subjectYaşam Bilimleri
dc.subjectMoleküler Biyoloji ve Genetik
dc.subjectKimya
dc.subjectBiyokimya
dc.subjectBiyoinorganik Kimya
dc.subjectFizikokimya
dc.subjectSpektroskopi
dc.subjectİnorganik Kimya
dc.subjectTemel Bilimler
dc.subjectMühendislik ve Teknoloji
dc.subjectComputer Sciences
dc.subjectChemical Engineering and Technology
dc.subjectLife Sciences
dc.subjectMolecular Biology and Genetics
dc.subjectChemistry
dc.subjectBiochemistry
dc.subjectBioinorganic Chemistry
dc.subjectPhysical Chemistry
dc.subjectSpectroscopy
dc.subjectInorganic Chemistry
dc.subjectNatural Sciences
dc.subjectEngineering and Technology
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectTemel Bilimler (SCI)
dc.subjectYaşam Bilimleri (LIFE)
dc.subjectBilgisayar Bilimi
dc.subjectMühendislik
dc.subjectMÜHENDİSLİK, KİMYASAL
dc.subjectKİMYA, İNORGANİK VE NÜKLEER
dc.subjectSPEKTROSKOPİ
dc.subjectKİMYA, FİZİKSEL
dc.subjectKİMYA, ORGANİK
dc.subjectEngineering, Computing & Technology (ENG)
dc.subjectNatural Sciences (SCI)
dc.subjectLife Sciences (LIFE)
dc.subjectCOMPUTER SCIENCE
dc.subjectENGINEERING
dc.subjectCHEMISTRY
dc.subjectMOLECULAR BIOLOGY & GENETICS
dc.subjectENGINEERING, CHEMICAL
dc.subjectCHEMISTRY, INORGANIC & NUCLEAR
dc.subjectSPECTROSCOPY
dc.subjectCHEMISTRY, PHYSICAL
dc.subjectCHEMISTRY, ORGANIC
dc.subjectKataliz
dc.subjectFizik Bilimleri
dc.subjectMoleküler Biyoloji
dc.subjectBilgisayar Bilimi Uygulamaları
dc.subjectFiziksel ve Teorik Kimya
dc.subjectOrganik Kimya
dc.subjectİnorganik kimya
dc.subjectCatalysis
dc.subjectPhysical Sciences
dc.subjectMolecular Biology
dc.subjectComputer Science Applications
dc.subjectPhysical and Theoretical Chemistry
dc.subjectOrganic Chemistry
dc.subjectaquatic toxicity
dc.subjectartificial neural network
dc.subjectbinding affinity
dc.subjectCORAL software
dc.subjectfullerene derivatives
dc.subjectfullerene-based nanomaterials
dc.subjectprotein–ligand binding activity
dc.subjectToxAlerts
dc.titleCheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives
dc.typearticle
dspace.entity.typePublication
local.avesis.id8248872d-ff69-4e42-970d-65d9fea27b57
local.indexed.atPUBMED
local.indexed.atSCOPUS
relation.isAuthorOfPublication99f2f09c-7e48-402f-9bb9-ccdd73c27ec2
relation.isAuthorOfPublication.latestForDiscovery99f2f09c-7e48-402f-9bb9-ccdd73c27ec2

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