Abstract
Quantitative structure-activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with "in silico" results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for "in silico environmental chemical science" are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.
Highlights
Progress in environmental chemical science is limited by the availability of data even more than most domains of science
The complexity of environmental conditions, combined with the diversity of substances that are of environmental concern, mean that direct measurements will never be sufficient to meet the data needs of environmental scientists or regulators
This level of accuracy is not satisfactory for some purposes, but it may be satisfactory for triaging among possible chemical reaction pathways or for descriptor data in QSAR development.[79,80]
Summary
The DFT calculated free energies of reaction for reactions involving bond breaking can have uncertainties of several kcal molÀ1 or more.[76,77] If the reaction occurs in aqueous solution, popular models of solvent effects (i.e. implicit solvent models) will contribute at least a few more kcal molÀ1 of uncertainty,[44] so overall errors of 5 kcal molÀ1 ($20 kJ molÀ1) or more are to be expected This level of accuracy is not satisfactory for some purposes (e.g., direct calculation of absolute values of speci c rate constants for contaminant degradation78), but it may be satisfactory for triaging among possible chemical reaction pathways or for descriptor data in QSAR development.[79,80] In addition, the overall accuracy of DFT calculations can be improved by using methods that make use of empirical additivity rules for molecular properties, where various properties of larger molecules can be thought of as being made up of additive contributions of atoms, bonds, or collections of atoms and bonds (i.e., functional groups) of the molecule.[81,82] These approaches have proven to be effective for small organic molecules,[83,84,85,86,87,88,89,90] and recently they have been used in advanced computational algorithms that can be used to simulate extremely large molecules, even including complex proteins and DNA chains.[91,92]. Mention of trade names or products does not convey, and should not be interpreted as official EPA approval, endorsement, or recommendation
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