Prediction of the Kováts Retention Indices of Thiols by Use of Quantum Chemical and Physicochemical Descriptors

Accurate prediction of isothermal gas chromatographic Kováts retention indices

We describe a freely available web server called Retention Index Predictor (RIpred) (https://ripred.ca) that rapidly and accurately predicts Gas Chromatographic Kováts Retention Indices (RI) using SMILES strings as chemical structure input. RIpred performs RI prediction for three different stationary phases (semi-standard non-polar (SSNP), standard non-polar (SNP), and standard polar (SP)) for both derivatized (trimethylsilyl (TMS) and tert‑butyldimethylsilyl (TBDMS) derivatized) and underivatized (base compound) forms of GC-amenable structures. RIpred was developed to address the need for freely available, fast, highly accurate RI predictions for a wide range of derivatized and underivatized chemicals for all common GC stationary phases. RIpred was trained using a Graph Neural Network (GNN) that used compound structures, their extracted features (mostly atom-level features) and the GC-RI data from the National Institute of Standards and Technology databases (NIST 17 and NIST 20). We curated this NIST 17 and NIST 20 GC-RI data, which is available for all three stationary phases, to create appropriate inputs (molecular graphs in this case) needed to enhance our model performance. The performance of different RIpred predictive models was evaluated using 10-fold cross validation (CV). The best performing RIpred models were identified and when tested on hold-out test sets from all stationary phases, achieved a Mean Absolute Error (MAE) of <73 RI units (SSNP: 16.5-29.5, SNP: 38.5-45.9, SP: 46.52-72.53). The Mean Absolute Percentage Error (MAPE) of these models were typically within 3% (SSNP: 0.78-1.62%, SNP: 1.87-2.88%, SP: 2.34-4.05%). When compared to the best performing model by Qu et al., 2021, RIpred performed similarly (MAE of 16.57 RI units [RIpred] vs. 16.84 RI units [Qu et al., 2021 predictor] for derivatized compounds). RIpred also includes ∼5 million predicted RI values for all GC-amenable compounds (∼57,000) in the Human Metabolome Database HMDB 5.0 (Wishart et al., 2022).

Relationship of air sampling rates of semipermeable membrane devices with the properties of organochlorine pesticides

The organochlorine pesticides (OCP) in Eastern-Barvaria at Haidel 1160 m a.s.l. were monitored with a low volume active air sampler and semi-permeable membrane devices (SPMD). The air sampling rates (Rair) of SPMD for OCP were calculated. Quantitative structure-property relationship (QSPR) models of Rair of SPMD were developed for OCP with partial least square (PLS) regression. Quantum chemical descriptors computed by semi-empirical PM6 method were used as predictor variables. The cumulative variance of the dependent variable explained by the PLS components and determined by cross-validation (Q(2)cum), for the optimal models, is 0.637, indicating that the model has good predictive ability and robustness, and could be used to estimate Rair values of OCP. The main factors governing Rair of OCP are intermolecular interactions and the energy required for cave-forming in dissolution of OCP into triolein of SPMD.