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Marine Drugs
Volume 17
Issue 11
10.3390/md17110624
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Article

Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach †

by
Giuseppe Floresta
1,*,
Davide Gentile
1,
Giancarlo Perrini
2,
Vincenzo Patamia
1 and
Antonio Rescifina
1,3,*
1
Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy
2
Department of Chemical Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy
3
Consorzio Interuniversitario Nazionale di ricerca in Metodologie e Processi Innovativi di Sintesi (C.I.N.M.P.S.), Via E. Orabona, 4, 70125 Bari, Italy
*
Authors to whom correspondence should be addressed.
†
In memory of Professor Carmela Spatafora, a friend, colleague and distinguished scientist, on the third anniversary of her premature death.
Mar. Drugs 2019, 17(11), 624; https://doi.org/10.3390/md17110624
Received: 4 September 2019 / Revised: 17 October 2019 / Accepted: 29 October 2019 / Published: 31 October 2019
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Abstract

Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have received interest following the recent publication of their pharmacologically beneficial effects. Recently, it was revealed that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered, but, unfortunately, none have reached the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FABP4 inhibitors; hence, different structure- and ligand-based computational approaches have been used for their identification. In this paper, we searched for new potentially active FABP4 ligands in the Marine Natural Products (MNP) database. We retrieved 14,492 compounds from this database and filtered through them with a statistical and computational filter. Seven compounds were suggested by our methodology to possess a potential inhibitory activity upon FABP4 in the range of 97–331 nM. ADMET property prediction was performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives. From these analyses, three molecules that are excellent candidates for becoming new drugs were found. View Full-Text
Keywords: FABP4; A-FABP; aP2; antidiabetes; antiobesity; antiatherosclerosis; anticancer; computational tools; computer-aided drug discovery FABP4; A-FABP; aP2; antidiabetes; antiobesity; antiatherosclerosis; anticancer; computational tools; computer-aided drug discovery
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MDPI and ACS Style

Floresta, G.; Gentile, D.; Perrini, G.; Patamia, V.; Rescifina, A. Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach. Mar. Drugs 2019, 17, 624. https://doi.org/10.3390/md17110624

AMA Style

Floresta G, Gentile D, Perrini G, Patamia V, Rescifina A. Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach. Marine Drugs. 2019; 17(11):624. https://doi.org/10.3390/md17110624

Chicago/Turabian Style

Floresta, Giuseppe, Davide Gentile, Giancarlo Perrini, Vincenzo Patamia, and Antonio Rescifina. 2019. "Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach" Marine Drugs 17, no. 11: 624. https://doi.org/10.3390/md17110624

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