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Mar. Drugs 2018, 16(7), 236; https://doi.org/10.3390/md16070236

Computational Methodologies in the Exploration of Marine Natural Product Leads

LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
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Received: 14 June 2018 / Revised: 2 July 2018 / Accepted: 6 July 2018 / Published: 13 July 2018
(This article belongs to the Special Issue Progress on Marine Natural Products as Lead Compounds)
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Abstract

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review. View Full-Text
Keywords: Computer-Aided Drug Design (CADD); drug discovery; chemoinformatics; bioinformatics; machine learning (ML); marine natural products (MNPs) Computer-Aided Drug Design (CADD); drug discovery; chemoinformatics; bioinformatics; machine learning (ML); marine natural products (MNPs)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Pereira, F.; Aires-de-Sousa, J. Computational Methodologies in the Exploration of Marine Natural Product Leads. Mar. Drugs 2018, 16, 236.

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