Correct identification of small molecules in complex samples is crucial for a variety of applications, including chemical forensics, soil science, and human health and disease. With the introduction of high-accuracy, high-throughput instrumentation, such as ion mobility spectrometry (IMS) coupled mass spectrometry, molecule identification can be done rapidly and with high confidence. However, identifications still rely on use of expensive and limited authentic reference standards (i.e., purified and validated chemical samples). One solution to alleviate this reliance on authentic standards is to build molecular feature libraries through computational simulation, as is being done with the in silico Chemical Library Engine (ISiCLE). When simulating molecular behavior in an IMS instrument, understanding gas-phase ion conformational populations is crucial, as subtle differences in conformation (same atoms and bonds, but different geometry) can lead to significantly different outcomes in property measurements, both computationally and empirically. Until now, there has not been a clear study of the most appropriate methods for conformer selection in computational pipelines. Here, we employ Monte Carlo simulation and physics-based approaches to perform thorough analysis of conformer sampling trends. We present our findings on the best selection technique, including the adequate number of samples as a function of quantified molecular rigidity, and discuss how this applies to current and future in silico molecular identification.

Optimal conformer selection for accurate in silico chemical property prediction

2019 Fall National Meeting

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