In silico structures, mass spectra and retention indices database development for purposes of chemical weapons convention

Abstract

The Chemical Weapons Convention (CWC) is a science-based international treaty for the disarmament and non-proliferation of chemical weapons. However, reference Orgaization for prohibition of chemical weapons (OPCW) central analytical database (OCAD) of structures with mass spectra (MS) and retention indices (RI) includes only minor part of all possible chemical species defined in the Schedules of CWC. In this work we employed OCAD in silico augmentation based on chemoinformatics approach for chemical structures enumeration, MS data generation based on message passing neural network and based on P-alkyl molecular pairs RI prediction to support the verification activities as provided for in the CWC. Enumerated 879 noncyclic and 5270 monocyclic alcohols became the basis for generating hundreds of thousands molecules of Schedule 1 toxic chemicals like Sarin, Tabun, VX and Novichok. Trained on ordinary and neutral loss electron ionization mass spectrometry (EI-MS) a message-passing neural network (MPNN) outperformed other quantum chemistry and machine learning methods. Generated by this MPNN in silico EI-MS are very similar to the library's spectra and allowed to reach desired match factor above 800 within a scale of 0–1000. Statistical data for molecular pairs based on P-alkyl fragments was collected and used to predict RIs within desired 20 RI window for some toxic chemicals of Schedule 1.A.01, for which in the current OCAD version RIs are absent.