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<p>We developed <i>SPARFlow</i>, an open-source <i>KNIME</i> workflow for structure–activity or structure–property relationship (SAR/SPR) analyses. The workflow integrates data preprocessing, chemical structure curation, similarity network construction, maximum common substructure detection, R-group decomposition, activity cliff identification, and database modelability assessment. It implements established indices, including SALI, SARI, MODI*, and RMODI, to characterize SAR landscapes and assess dataset suitability for predictive modeling. <i>SPARFlow</i> was validated using four datasets with distinct chemical and endpoint characteristics: cruzain inhibitors, biased μ-opioid receptor agonists, pesticides, and carbonyl compounds with hydration constants.</p><p>Scientific Contribution</p><p>This work introduces <i>SPARFlow</i>, an <i>KNIME</i>-integrated workflow that combines data curation, activity-cliff detection, and modelability assessment for SAR and SPR studies. The workflow provides a unified implementation of key SAR analyses within a single KNIME pipeline. It updates implementations of established metrics, including MODI* and RMODI, together with complementary indices such as SARI and SALI. It ensures consistent data flow across all modules.</p> Graphical Abstract <p></p>

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SPARFlow: a KNIME workflow for integrated structure–activity or structure–property relationship analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Elier E. Abreu-Martínez,
  • Karina Martinez-Mayorga,
  • Gabriel Merino

摘要

We developed SPARFlow, an open-source KNIME workflow for structure–activity or structure–property relationship (SAR/SPR) analyses. The workflow integrates data preprocessing, chemical structure curation, similarity network construction, maximum common substructure detection, R-group decomposition, activity cliff identification, and database modelability assessment. It implements established indices, including SALI, SARI, MODI*, and RMODI, to characterize SAR landscapes and assess dataset suitability for predictive modeling. SPARFlow was validated using four datasets with distinct chemical and endpoint characteristics: cruzain inhibitors, biased μ-opioid receptor agonists, pesticides, and carbonyl compounds with hydration constants.

Scientific Contribution

This work introduces SPARFlow, an KNIME-integrated workflow that combines data curation, activity-cliff detection, and modelability assessment for SAR and SPR studies. The workflow provides a unified implementation of key SAR analyses within a single KNIME pipeline. It updates implementations of established metrics, including MODI* and RMODI, together with complementary indices such as SARI and SALI. It ensures consistent data flow across all modules.

Graphical Abstract