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

作者：Witek Jagna; Smusz Sabina; Rataj Krzysztof; Mordalski Stefan; Bojarski Andrzej J^*

来源：Bioorganic & Medicinal Chemistry Letters, 2014, 24(2): 580-585.

DOI：10.1016/j.bmcl.2013.12.017

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

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.

• 出版日期2014-1-15