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 varying definitions trend with mass spectrometry (MS)-based observability, that is, the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA’s nontargeted analysis collaborative trial, where blinded samples had been analyzed previously, providing 1398 data points. Improved understanding of observability would offer many advantages in small-molecule identification, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property nontrivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices

“…However, for medical devices, limited information describing quality criteria for nonpublic databases has been described, and no known consensus standards for such approaches are currently available. Examples of potential criteria for expert review of individual library spectra include the presence of chemical structure information, spectral quality grading, spectral interpretation (based on suspected structure and fragmentation mechanisms), spectral labeling, and removal of spectral peaks unrelated to the analyte of interest. , Additionally, review of the extent to which the chemical scope of the spectral library matches the chemical space of interest has been reported, and methods are emerging on how to evaluate the chemical space of libraries …”