Progress in visual representations of chemical space

Area of Science:

• Chemoinformatics
• Computational Chemistry
• Data Visualization

Background:

• Chemical space, encompassing discrete molecules and descriptor space, is fundamental to molecular analysis.
• Applications include compound database analysis, library design, and structure-activity relationship (SAR) studies.
• Diverse visualization techniques exist, each with specific applicability domains and requiring expert selection.

Purpose of the Study:

• To review recent advancements in chemical space visualization.
• To present these methods within the current understanding of chemical space.
• To provide notable application examples.

Main Methods:

• Review of visualization techniques such as van Krevelen diagrams and descriptor plots.
• Discussion of dimensionality reduction methods like principal components analysis (PCA) and self-organizing maps (SOM).
• Inclusion of generative topographic mapping (GTM) and graph/network-based approaches.

Main Results:

• Recent advances in chemical space visualization methods are presented.
• Various techniques, including PCA, SOM, and GTM, are detailed with examples.
• The utility of these representations is increasing with computational power.

Conclusions:

• Visualization tools are becoming essential for chemoinformaticians handling large datasets.
• A variety of methods are available, allowing scientists to choose task-specific approaches.
• No single universal representation exists; expert knowledge remains critical for effective application.