In recent years there has been a paradigm shift in how data is being used to progress early
drug discovery campaigns from hit identification to candidate selection. Significant developments in
data mining methods and the accessibility of tools for research scientists have been instrumental in
reducing drug discovery timelines and in increasing the likelihood of a chemical entity achieving
drug development milestones. KNIME, the Konstanz Information Miner, is a leading open source
data analytics platform and has supported drug discovery endeavours for over a decade. KNIME provides
a rich palette of tools supported by an extensive community of contributors to enable ligandand
structure-based drug design. This review will examine recent developments within the KNIME
platform to support small-molecule drug design and provide a perspective on the challenges and future
developments within this field.
Keywords: Hit expansion, virtual screening, predictive toxicology, ligand optimisation, data mining, KNIME,
ADME modelling, big data, workflows, computer-aided drug design.
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