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Article

Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach

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Associate Laboratory i4HB—Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
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UCIBIO—Applied Molecular Biosciences Unit, Department of Chemistry, Blue Biotechnology and Biomedicine Lab, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
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LAQV, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Orazio Taglialatela-Scafati
Mar. Drugs 2022, 20(2), 129; https://doi.org/10.3390/md20020129
Received: 13 December 2021 / Revised: 28 January 2022 / Accepted: 6 February 2022 / Published: 8 February 2022
(This article belongs to the Special Issue Marine Drug Discovery through Computer-Aided Approaches)
Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure–activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar’s website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives. View Full-Text
Keywords: marine natural products (MNPs); blue biotechnology; quantitative structure–activity relationship (QSAR); machine learning (ML) techniques; computer-aided drug design (CADD); molecular docking; virtual screening; antifouling activity; acetylcholinesterase enzyme (AChE) marine natural products (MNPs); blue biotechnology; quantitative structure–activity relationship (QSAR); machine learning (ML) techniques; computer-aided drug design (CADD); molecular docking; virtual screening; antifouling activity; acetylcholinesterase enzyme (AChE)
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MDPI and ACS Style

Gaudêncio, S.P.; Pereira, F. Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach. Mar. Drugs 2022, 20, 129. https://doi.org/10.3390/md20020129

AMA Style

Gaudêncio SP, Pereira F. Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach. Marine Drugs. 2022; 20(2):129. https://doi.org/10.3390/md20020129

Chicago/Turabian Style

Gaudêncio, Susana P., and Florbela Pereira. 2022. "Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach" Marine Drugs 20, no. 2: 129. https://doi.org/10.3390/md20020129

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