Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets.

ABSTRACT: Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader "chemical space." These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable Python module, chespa, built to easily assess different chemical space definitions through clustering of compounds in these spaces and visualizing trends of these clusters. To demonstrate this, chespa currently streamlines prediction of various molecular descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test six different chemical space definitions. Furthermore, we investigated how these vary

SUBMITTER: Nunez JR 

PROVIDER: S-EPMC9648166 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature