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Molecules 2018, 23(5), 1137;

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands

Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Krakow, Poland
Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H1117 Budapest, Hungary
Grupo de Investigación “BioFarma” USC, Centro de Investigación CIMUS, Planta 3ª, Avd. de Barcelona s/n, 15782 Santiago de Compostela, Spain
Authors to whom correspondence should be addressed.
Academic Editor: F. Javier Luque
Received: 16 April 2018 / Revised: 5 May 2018 / Accepted: 7 May 2018 / Published: 10 May 2018
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods, docking, and multiple scoring methods. First, we applied machine learning tools to filter a large database of druglike compounds by the new Neighbouring Substructures Fingerprint (NSFP). This two-dimensional fingerprint contains information on the connectivity of the substructural features of a compound. Preselected subsets of the database were then subjected to docking calculations. The main indicators of compounds’ selectivity were their different interactions with the secondary binding pockets of both target proteins, while binding modes within the orthosteric binding pocket were preserved. The combined methodology of ligand-based and structure-based methods was validated prospectively, resulting in the identification of hits with nanomolar affinity and ten-fold to ten thousand-fold selectivities. View Full-Text
Keywords: target selectivity; G-protein coupled receptor; 5-HT2BR; chemical fingerprint target selectivity; G-protein coupled receptor; 5-HT2BR; chemical fingerprint

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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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Rataj, K.; Kelemen, Á.A.; Brea, J.; Loza, M.I.; Bojarski, A.J.; Keserű, G.M. Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands. Molecules 2018, 23, 1137.

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