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Comprehensive and confident identifications of metabolites and otherchemicals in complex samples will revolutionize our understanding of the rolethese chemically diverse molecules play in biological systems. Despite recentadvances, metabolomics studies still result in the detection of adisproportionate number of features than cannot be confidently assigned to achemical structure. This inadequacy is driven by the single most significantlimitation in metabolomics: the reliance on reference libraries constructed byanalysis of authentic reference chemicals. To this end, we have developed thein silico chemical library engine (ISiCLE), a high-performancecomputing-friendly cheminformatics workflow for generating libraries ofchemical properties. In the instantiation described here, we predict probablethree-dimensional molecular conformers using chemical identifiers as input,from which collision cross sections (CCS) are derived. The approach employsstate-of-the-art first-principles simulation, distinguished by use of moleculardynamics, quantum chemistry, and ion mobility calculations to generatestructures and libraries, all without training data. Importantly, optimizationof ISiCLE included a refactoring of the popular MOBCAL code fortrajectory-based mobility calculations, improving its computational efficiencyby over two orders of magnitude. Calculated CCS values were validated against1,983 experimentally-measured CCS values and compared to previously reportedCCS calculation approaches. An online database is introduced for sharing bothcalculated and experimental CCS values (metabolomics.pnnl.gov), initiallyincluding a CCS library with over 1 million entries. Finally, three successfulapplications of molecule characterization using calculated CCS are described.This work represents a promising method to address the limitations of smallmolecule identification.

ISiCLE: A molecular collision cross section calculation pipeline for establishing large in silico reference libraries for compound identification