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

Area of Science:

• * Computational chemistry and cheminformatics.
• * Drug discovery and development.
• * Quantitative structure-activity relationship (QSAR) studies.

Background:

• * Structure-activity relationship (SAR) and structure-property relationship (SPR) analyses are crucial for understanding molecular interactions and predicting compound behavior.
• * Existing tools for SAR/SPR analysis are often fragmented, requiring integration of multiple software packages.
• * Assessing dataset modelability and identifying activity cliffs are essential steps for successful predictive modeling in drug discovery.

Purpose of the Study:

• * To develop an integrated, open-source workflow for comprehensive SAR/SPR analyses.
• * To provide a unified platform within KNIME for data preprocessing, chemical curation, and SAR landscape characterization.
• * To implement and update established metrics for assessing dataset modelability and identifying critical structural features.

Main Methods:

• * Development of SPARFlow, an open-source KNIME workflow.
• * Integration of modules for data preprocessing, chemical structure curation, similarity network construction, maximum common substructure detection, and R-group decomposition.
• * Implementation of indices such as SALI, SARI, MODI*, and RMODI for SAR landscape characterization and modelability assessment.

Main Results:

• * SPARFlow successfully integrates diverse SAR/SPR analysis techniques into a single KNIME pipeline.
• * The workflow provides updated implementations of key metrics like MODI* and RMODI, alongside SARI and SALI.
• * Validation across four diverse datasets (cruzain inhibitors, opioid agonists, pesticides, carbonyl compounds) demonstrated the workflow's applicability and robustness.

Conclusions:

• * SPARFlow offers a valuable, unified solution for SAR/SPR analyses, enhancing efficiency and consistency.
• * The workflow aids researchers in evaluating dataset suitability for predictive modeling and identifying key structural drivers of activity or properties.
• * This open-source tool facilitates robust cheminformatics analyses in drug discovery and related fields.