{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Miller TH"],"funding":["AstraZeneca","Biotechnology and Biological Sciences Research Council"],"pagination":["7973-81"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5089532"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["50(15)"],"pubmed_abstract":["Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d(-1) (RTD-model) and 0.03 ± 0.03 L d(-1) (GSD-model) relative to experimentally d"],"journal":["Environmental science & technology"],"pubmed_title":["The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS)."],"pmcid":["PMC5089532"],"funding_grant_id":["1732933","BB/K501177/1"],"pubmed_authors":["Reid MJ","Barron LP","Harman C","Thomas KV","Baz-Lomba JA","Bury NR","Miller TH","Owen SF"],"additional_accession":[]},"is_claimable":false,"name":"The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS).","description":"Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d(-1) (RTD-model) and 0.03 ± 0.03 L d(-1) (GSD-model) relative to experimentally d","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Aug","modification":"2025-04-26T06:16:35.443Z","creation":"2019-03-27T02:27:58Z"},"accession":"S-EPMC5089532","cross_references":{"pubmed":["27363449"],"doi":["10.1021/acs.est.6b01407"]}}