Q-raKtion: A Semiautomated KNIME Workflow for Bioactivity Data Points Curation

Area of Science:

• Computational chemistry
• Cheminformatics
• Drug discovery

Background:

• Vast amounts of bioactivity data are available from chemogenomic repositories, supporting predictive model development.
• Inconsistent data from multiple sources hinders the effective use of this information, necessitating data harmonization.
• Automated pipelines for chemical data exist, but tools for bioactivity data curation are lacking.

Purpose of the Study:

• To address the gap in bioactivity data curation tools.
• To provide a solution for end-users to build high-quality, proprietary datasets.
• To facilitate the integration and harmonization of compound bioactivity information.

Main Methods:

• Development of Q-raKtion, a systematic and semi-automated KNIME workflow.
• Aggregation of biological activity data from PubChem and ChEMBL repositories.
• Customization features to meet specific end-user needs for data processing.

Main Results:

• Q-raKtion effectively aggregates compound bioactivity information from major public databases.
• The workflow provides a flexible and customizable approach to data harmonization.
• It serves as a foundational tool for creating reliable datasets for predictive modeling.

Conclusions:

• Q-raKtion represents a significant step towards addressing the need for bioactivity data curation tools.
• The tool enables researchers to overcome data uniformity challenges from public repositories.
• It supports the development of more robust predictive models through high-quality, harmonized datasets.