Physical Absorption Of CO2 in Protic and Aprotic Ionic Liquids: An Interaction Perspective.

The physical absorption of CO2 by protic and aprotic ionic liquids such as 1-ethyl-3-methyl-imidazolium tetrafluoroborate was examined at the molecular level using symmetry adapted perturbation theory (SAPT) and density functional techniques through comparison of interaction energies of noncovalently bound complexes between the CO2 molecule and a series of ionic liquid ions and ion pairs. These energies were contrasted with those for complexes with model amines such as methylamine, dimethylamine, and trimethylamine. Detailed analysis of the five fundamental forces that are responsible for stabilization of the complexes is discussed. It was confirmed that the nature of the anion had a greater effect upon the physical interaction energy in non functionalized ionic liquids, with dispersion forces playing an important role in CO2 solubility. Hydrogen bonding with protic cations was shown to impart additional stability to the noncovalently bound CO2···IL complex through inductive forces. Two solvation models, the conductor-like polarizable continuum model (CPCM) and the universal solvation model (SMD), were used to estimate the impact of solvent effects on the CO2 binding. Both solvent models reduced interaction energies for all types of ions. These interaction energies appeared to favor imidazolium cations and carboxylic and sulfonic groups as well as bulky groups (e.g., NTf2) in anions for the physical absorption of CO2. The structure-reactivity relationships determined in this study may help in the optimization of the physical absorption process by means of ionic liquids.

Potential glucocorticoid receptor ligands with pulmonary selectivity using I-QSAR with the signature molecular descriptor.

We intend in this research to establish a rational method for the development of novel glucocorticoid receptor ligands to more effectively prevent respiratory inflammation. Corticosteroids, a class of steroid hormones, are naturally inclined to bind to the glucocorticoid receptor and, in this research, are the basis for exploring other novel and non-intuitive structures. To be more effective than currently available medications, novel compounds must be highly selective toward the lungs and must be inactivated when exposed to the main circulation, thus preventing the participation of the ligand in other systems and consequently reducing systemic side-effects. We look to use the inverse-quantitative structure-activity relationship algorithm with the Signature molecular descriptor to generate new ligands based upon the structures and activities of 65 experimentally studied corticosteroids. Inverse-quantitative structure-activity relationship explore many possible combinations of atom connectivity while structural filters and other scoring approaches are used to predict and identify the most promising candidates for further study. Properties explored include high receptor binding affinity, high systemic clearance, high plasma protein binding and low oral bioavailability. Among more than 300 million potential candidates generated, 84 high priority compounds with properties predicted to be at least as or more effective than currently available corticosteroids have been identified with this procedure.

Data mining PubChem using a support vector machine with the Signature molecular descriptor: classification of factor XIa inhibitors.

The amount of high-throughput screening (HTS) data readily available has significantly increased because of the PubChem project (http://pubchem.ncbi.nlm.nih.gov/). There is considerable opportunity for data mining of small molecules for a variety of biological systems using cheminformatic tools and the resources available through PubChem. In this work, we trained a support vector machine (SVM) classifier using the Signature molecular descriptor on factor XIa inhibitor HTS data. The optimal number of Signatures was selected by implementing a feature selection algorithm of highly correlated clusters. Our method included an improvement that allowed clusters to work together for accuracy improvement, where previous methods have scored clusters on an individual basis. The resulting model had a 10-fold cross-validation accuracy of 89%, and additional validation was provided by two independent test sets. We applied the SVM to rapidly predict activity for approximately 12 million compounds also deposited in PubChem. Confidence in these predictions was assessed by considering the number of Signatures within the training set range for a given compound, defined as the overlap metric. To further evaluate compounds identified as active by the SVM, docking studies were performed using AutoDock. A focused database of compounds predicted to be active was obtained with several of the compounds appreciably dissimilar to those used in training the SVM. This focused database is suitable for further study. The data mining technique presented here is not specific to factor XIa inhibitors, and could be applied to other bioassays in PubChem where one is looking to expand the search for small molecules as chemical probes.