title: A quantitative structure-activity relationship to predict efficacy of granular activated carbon adsorption to control emerging contaminants.
abstract: A quantitative structure-activity relationship was developed to predict the efficacy of carbon adsorption as a control technology for endocrine-disrupting compounds, pharmaceuticals, and components of personal care products, as a tool for water quality professionals to protect public health. Here, we expand previous work to investigate a broad spectrum of molecular descriptors including subdivided surface areas, adjacency and distance matrix descriptors, electrostatic partial charges, potential energy descriptors, conformation-dependent charge descriptors, and Transferable Atom Equivalent (TAE) descriptors that characterize the regional electronic properties of molecules. We compare the efficacy of linear (Partial Least Squares) and non-linear (Support Vector Machine) machine learning methods to describe a broad chemical space and produce a user-friendly model. We employ cross-validation, y-scrambling, and external validation for quality control. The recommended Support Vector Machine model trained on 95 compounds having 23 descriptors offered a good balance between good performance statistics, low error, and low probability of over-fitting while describing a wide range of chemical features. The cross-validated model using a log-uptake (qe) response calculated at an aqueous equilibrium concentration (Ce) of 1 μM described the training dataset with an r(2) of 0.932, had a cross-validated r(2) of 0.833, and an average residual of 0.14 log units.
authors: a Department of Civil and Environmental Engineering , Rensselaer Polytechnic Institute , Troy , NY , USA.
authors: A R
authors: AR
authors: Kennicutt
authors: b Department of Chemistry and Chemical Biology , Rensselaer Polytechnic Institute , Troy , NY , USA.
authors: L
authors: L
authors: Morkowchuk
authors: b Department of Chemistry and Chemical Biology , Rensselaer Polytechnic Institute , Troy , NY , USA.
authors: M
authors: M
authors: Krein
authors: b Department of Chemistry and Chemical Biology , Rensselaer Polytechnic Institute , Troy , NY , USA.
authors: C M
authors: CM
authors: Breneman
authors: a Department of Civil and Environmental Engineering , Rensselaer Polytechnic Institute , Troy , NY , USA.
authors: J E
authors: JE
authors: Kilduff
mesh_terms: D000327:Adsorption; D002606:Charcoal / Q000737:chemistry*; D052244:Endocrine Disruptors / Q000737:chemistry; D016018:Least-Squares Analysis; D004364:Pharmaceutical Preparations / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine; D014874:Water Pollutants, Chemical / Q000737:chemistry*
chemical_list: D052244:Endocrine Disruptors; D004364:Pharmaceutical Preparations; D014874:Water Pollutants, Chemical; D002606:Charcoal
keywords: Adsorption; QSAR; activated carbon; endocrine disruptor; pharmaceutical; support vector machine