In the light of recent developments in computer technology, a promising and efficient way to design a material with a desired property would be to solve the inverse problem: use a physical property to predict structure. Here, we discuss the basic idea and mathematical foundation of the inverse approach, and proposed strategies for its utilization in the design of materials over nano‐ to macro‐scales. At the nano‐scale, analyzed strategies include scanning of a high‐dimensional space of chemical compounds for those compounds that have a targeted property, and identification of correlations in large databases of materials. However, unlike utilization of inverse approach at nano‐scale where full structural information ‐ atoms and their positions‐ is linked to targeted properties, at the meso‐ and macro‐scale, only partial structural information, manifested via structural motifs or representative volume elements, is available. We discuss the role of partial structural information in the inverse approach to the design of materials at those scales. Risks and limitations of the inverse approach are analyzed and dependence of the approach on factors such as structure parametrization, approximations in theoretical models, and feedback from structural characterization, is addressed.
Abstract The Maillard (browning) reaction involving the polycondensation of sugars and amino acids is believed to be an important abiotic pathway for humic substance formation in nature. However, a major drawback is that the Maillard reaction is extremely slow at temperatures encountered under normal environmental conditions. In order to elucidate some details of this process molecular shape analysis was applied to investigate the initial reaction between D-glucose and glycine to form the Amadori compound fructosylglycine which is an intermediate product in the Maillard reaction. The structure of the Amadori compound was optimized at a quantum mechanical level and its ground state electron energy calculated. Molecular Iso-Density Contours (MIDCO's), electron density contour surfaces of constant electron density, were constructed for D-glucose, glycine and fructosylglycine in order to study the steric conditions for the reaction. The calculations indicate that the Amadori compound and water on one hand and the separate entities D-glucose and glycine on the other hand are very similiar to each other in terms of their ground state energy. This agrees with the experimental observation that the reaction between D-glucose and glycine to form the Amadori compound is slow. 相似文献
Structure–activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects. 相似文献
