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The influence of hashed fingerprints density on the machine learning methods performance
2013
Journal of Cheminformatics
Computational techniques have become a vital part of today's drug discovery campaigns. Among a wide range of tools applied in this process, machine learning methods can be distinguished. They are used for instance in virtual screening (VS), where its role is to identify potentially active compounds out of large libraries of structures [1] . In order to enable the application of various learning algorithms in VS tasks, an appropriate representation of molecules is needed. One of the solutions
doi:10.1186/1758-2946-5-s1-p25
pmcid:PMC3606238
fatcat:yopjg5jmfrbvtbko35ck3mbkxy