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Bioorg Med Chem Lett. 2014 Jan 15;24(2):580-5. doi: 10.1016/j.bmcl.2013.12.017. Epub 2013 Dec 10.

An application of machine learning methods to structural interaction fingerprints--a case study of kinase inhibitors.

Author information

1
Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, Kraków 31-343, Poland.
2
Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, Kraków 31-343, Poland; Faculty of Chemistry, Jagiellonian University, Ingardena 3, Kraków 30-060, Poland.
3
Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, Kraków 31-343, Poland. Electronic address: bojarski@if-pan.krakow.pl.

Abstract

In this Letter, we present a novel methodology of searching for biologically active compounds, which is based on the combination of docking experiments and analysis of the results by machine learning methods. The study was performed for 5 different protein kinases, and several sets of compounds (active, inactive and assumed inactives) were docked into their targets. The resulting ligand-protein complexes were represented by the means of structural interaction fingerprints profiles (SIFts profiles) that constituted an input for ML methods. The developed protocol was found to be superior to the combination of classification algorithms with the standard fingerprint MACCSFP.

KEYWORDS:

Docking results analysis; Machine learning; Structural interaction fingerprint; Virtual screening

PMID:
24374279
DOI:
10.1016/j.bmcl.2013.12.017
[Indexed for MEDLINE]

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