Technological advances in high-throughput screening methods, combinatorial chemistry and the design of virtual
libraries have evolved in the pursuit of challenging drug targets. Over the last two decades a vast amount of data has
been generated within these fields and as a consequence data mining methods have been developed to extract key pieces
of information from these large data pools. Much of this data is now available in the public domain. This has been helpful
in the arena of drug discovery for both academic groups and for small to medium sized enterprises which previously
would not have had access to such data resources. Commercial data mining software is sometimes prohibitively expensive
and the alternate open source data mining software is gaining momentum in both academia and in industrial applications
as the costs of research and development continue to rise. KNIME, the Konstanz Information Miner, has emerged as a
leader in open source data mining tools. KNIME provides an integrated solution for the data mining requirements across
the drug discovery pipeline through a visual assembly of data workflows drawing from an extensive repository of tools.
This review will examine KNIME as an open source data mining tool and its applications in drug discovery.
Keywords: Bioinformatics, cheminformatics, computational chemistry, data mining, drug discovery, KNIME.
Rights & PermissionsPrintExport