Using theoretical descriptors in quantitative structure-activity relationships: HPLC capacity factors for energetic materials

Simulation of chromatography of phenolic compounds with a computational chemical method

An ab initio simulation of reversed-phase liquid chromatography for phenolic compounds was achieved based on molecular interaction energy values calculated using molecular mechanics calculations (MM2) of the CAChe program. The precision of the predicted retention factors from the molecular interaction energy values was equivalent to the predicted retention factors based on octanol-water partition coefficients (log P) calculated using the molecular orbital package (MOPAC). The prediction of retention factors of phenolic compounds in reversed-phase liquid chromatography in a given pH eluent was possible using the predicted dissociation constant (pKa) from the atomic partial charge without a chemical experiment if the organic modifier effect was known.

High-throughput screening and quantitative structure-efficacy relationship models of potential displacer molecules for ion-exchange systems

A technique is presented for the high-throughput screening of ion-exchange displacers. Potential displacers were employed to displace proteins in parallel batch ion-exchange experiments. The percentage of protein displaced from a particular stationary phase was then used as a parameter to rank the displacers. By employing this technique, a large number of molecules possessing a range of affinities and properties could be rapidly evaluated. This data was then used together with traditional and electron density-based transferable atom equivalent (TAE) molecular descriptors computed for the displacer molecules to produce quantitative structure-efficacy relationship (QSER) models using a genetic algorithm/partial least squares (GA/PLS) regression approach. The QSER models were generated using a portion of the protein-displacement data, with the remainder serving as a test set. Descriptor selection and model building was accomplished using a genetic algorithm/partial least squares approach. The resulting models were found to have high-correlation coefficients and could be used to accurately predict the behavior of molecules not included in the training set. In addition, the models were employed to examine a virtual library of displacers based on modifications of neomycin to provide further insight into displacer design. The results presented here indicate that it may be possible to design displacers that can dramatically improve the effective selectivity of ion-exchange chromatographic materials.

Application of neural networks in structure-activity relationships

Methodology and application of artificial neural networks in structure-activity relationships are reviewed focusing on the most frequently used three-layer feedforward back-propagation procedure. Two applications of neural networks are presented and a comparison of the performance with those of CoMFA and a classical QSAR analysis is also discussed.