Constellation Plots in KNIME: An Automated Scaffold-Based Workflow for Interactive Chemical Space Visualization

Area of Science:

• Computational Chemistry
• Cheminformatics
• Drug Discovery

Background:

• Chemical space analysis is crucial for understanding compound properties and relationships.
• Constellation Plots offer a high-density visualization of chemical space by integrating molecular structure, properties, and frequency.
• Existing methods for generating Constellation Plots lack user-friendliness and automation, posing a barrier for non-computational experts.

Purpose of the Study:

• To develop an intuitive, automated, and scaffold-based workflow for generating Constellation Plots.
• To facilitate chemical space visualization and analysis for a broader range of researchers.
• To provide a generalizable tool applicable to various chemical datasets with annotated properties.

Main Methods:

• Development of an interactive and automated workflow within the KNIME open-source platform.
• Utilizing scaffold-based analysis for chemical space representation.
• Application of dimensionality reduction algorithms for visualization.

Main Results:

• Successful implementation of a user-friendly, low-code protocol for automated Constellation Plot generation.
• Demonstration of the workflow's utility with a dataset of 5,211 Tau protein inhibitors for Alzheimer's disease research.
• Creation of a versatile resource for analyzing diverse chemical datasets with associated properties.

Conclusions:

• The developed KNIME workflow significantly enhances the accessibility and efficiency of chemical space visualization using Constellation Plots.
• This tool empowers researchers, including those with limited computational expertise, to analyze complex molecular data.
• The workflow serves as a valuable, freely available resource for advancing drug discovery and other chemistry-related fields.