ChemMaps: Towards an approach for visualizing the chemical space based on adaptive satellite compounds

Area of Science:

• Computational Chemistry
• Cheminformatics
• Data Visualization

Background:

• Visualizing large chemical spaces is crucial for drug discovery and materials science.
• Current methods can be computationally intensive and may not scale efficiently.
• Representing molecular similarity effectively is key to understanding chemical diversity.

Purpose of the Study:

• To introduce ChemMaps, a novel and efficient approach for chemical space visualization.
• To demonstrate the utility of a 'satellites' approach for representing chemical space.
• To assess the performance of ChemMaps across diverse chemical datasets.

Main Methods:

• Utilizing molecular fingerprints to calculate similarity matrices for compound datasets.
• Employing a 'satellites' strategy where selected molecules represent the overall chemical space.
• Iteratively increasing the number of satellites until high correlation between visualizations is achieved.

Main Results:

• ChemMaps demonstrates efficient chemical space visualization, especially for datasets with low 2D diversity.
• The number of satellites required is dataset-dependent, with as few as 25% being sufficient for less diverse sets.
• 3D diversity plays a secondary role but becomes more relevant with increasing 2D diversity.

Conclusions:

• The 'satellites' approach in ChemMaps offers a more efficient method for chemical space visualization.
• The method's effectiveness is influenced by dataset diversity, particularly 2D diversity.
• This proof-of-principle study lays the groundwork for broader applications of ChemMaps.