Sampling and Mapping Chemical Space with Extended Similarity Indices

Area of Science:

• Chemistry
• Cheminformatics
• Computational Chemistry

Background:

• Chemical space visualization aids compound library design, diversity analysis, and structure-property relationship exploration.
• Applications are prominent in drug discovery and natural product research.
• Navigating large chemical datasets requires approximations due to high dimensionality.

Purpose of the Study:

• To introduce ChemMaps, a visualization methodology for approximating large chemical datasets.
• To demonstrate how extended similarity indices can enhance ChemMaps.
• To reduce the high-dimensional data needed to describe chemical libraries.

Main Methods:

• ChemMaps approximates chemical space distribution using selected satellite compounds.
• Principal component analysis on a similarity matrix is performed on selected satellites.
• Extended similarity indices are employed to identify relevant sampling regions.

Main Results:

• ChemMaps effectively approximates large chemical dataset distributions.
• Extended similarity indices aid in selecting informative satellite compounds.
• The methodology reduces the dimensionality of chemical library data.

Conclusions:

• ChemMaps provides a viable approach for navigating and understanding large chemical spaces.
• Extended similarity indices offer an improvement for satellite selection in ChemMaps.
• This approach facilitates more efficient exploration of chemical libraries.