No abstract

A designer Quantitative Structure-Property Relationship, based upon molecular properties calculated using the AM1 semiempirical quantum mechanical method, was developed to predict the glass transition temperature of amine-cured epoxy resins based on the diglycidyl ether of bisphenol A. The QSPR (R2 = 0.9977) was generated using the regression analysis program, COmprehensive DEscriptors for Structural and Statistical Analysis. By applying an ad hoc treatment based on the elementary probability theory to the quantitative structure-property relationship analysis a method was developed for computing bulk polymer glass transition temperatures for stoichiometric and nonstoichiometric monomeric formulations. A model polymer was synthesized and found to validate our model predictions.

X-ray crystallographic determinations and AM1 calculations have defined the solid-state and gas-phase structures of cyclotri(deoxycholate) and cyclotetra(24-norcholate). The latter cyclotetramer is one of the largest open macrocycles ever subjected to crystallography.

The objective of this study was to identify through quantum mechanical quantitative structure activity relationships (Q-QSARs) chemical structures in dental monomers that influence their mutagenicity. AMPAC, a semiempirical computer program that provides quantum mechanical information for chemical structures, was applied to three series of reference chemicals: a set of methacrylates, a set of aromatic and a set of aliphatic epoxy compounds. QSAR models were developed using this chemical information together with mutagenicity data (Salmonella TA 100, Ames Test). CODESSA, a QSAR program that calculates quantum chemical descriptors from information generated by AMPAC and statistically matches these descriptors with observed biological properties was used. QSARs were developed which had r2 values exceeding 0.90 for each study series. These QSARs were used to accurately predict the mutagenicity of BISGMA. a monomer commonly used in dentistry, and two epoxy monomers with developing use in dentistry, GY-281 and UVR-6105. The Q-QSAR quantum mechanical descriptors correctly predicted the level of mutagenicity for all three compounds. The descriptors in the correlation equation pointed to components of structure that may contribute to mutagenesis. The QSARs also provided 'dose windows' for testing mutagenicity, circumventing the need for extensive dose exploration in the laboratory. The Q-QSAR method promises an approach for biomaterials scientists to predict and avoid mutagenicity from the chemicals used in new biomaterial designs.

The accurate prediction of the melting temperature of organic compounds is a significant problem that has eluded researchers for many years. The most common approach used to develop predictive models entails the derivation of quantitative structure-property relationships (QSPRs), which are multivariate linear relationships between calculated quantities that are descriptors of molecular or electronic features and a property of interest. In this report the derivation of QSPRs to predict melting temperatures of energetic materials based on descriptors calculated using the AM1 semiempirical quantum mechanical method are described. In total, the melting points and experimental crystal structures of 148 energetic materials were analyzed. Principal components analysis was performed in order to assess the relative importance and roles of the descriptors in our QSPR models. Also described are the results of k means cluster analysis, performed in order to identify natural groupings within our study set of structures. The QSPR models resulting from these analyses gave training set R(2) values of 0.6085 (RMSE = ± 15.7 °C) and 0.7468 (RMSE = ± 13.2 °C). The test sets for these clusters had R(2) values of 0.9428 (RMSE = ± 7.0 °C) and 0.8974 (RMSE = ± 8.8 °C), respectively. These models are among the best melting point QSPRs yet published for energetic materials.