ADAPT geometric descriptors

Taken globally, the results show a remarkable adaptability of acetylcholine which can be justified considering both its intrinsic flexibility, and the fact that its intramolecular interactions are not very strong and that almost all media can compete with them. Such adaptability finds a noteworthy implication in significant pairwise correlations between physicochemical properties and geometrical descriptors as well as among physicochemical properties. Thus, Fig. 1.6 shows the revealing 3D... 

An automatic version of MOLMEC has also been developed so that data sets with large numbers of molecules can be modelled without continuous supervision. The program consists of an input section, which reads the molecule s connection table and present coordinate matrix from the ADAPT disc files, a minimization section with all output suppressed, and a section which stores the final coordinate matrix. Good models can easily be obtained in this manner. However, before the coordinate matrices can be used for calculating descriptors, the structures are reviewed to make sure that the molecules are in acceptable conformations, Once modelling is complete, geometric descriptors can be derived. 

There are three general classes of descriptors topological, geometrical, and physicochemical. Topological descriptors are derived from the topological representation of the structure, the connection table. The geometrical descriptors are derived from the three dimensional model of the molecule. Physicochemical descriptors may be measured experimentally, calculated using a mathematical model, or represented by linearly correlated calculated descriptors. The descriptors that are currently available in ADAPT are as follows ... 

Jurs and co-workers have used parameters generated by the ADAPT system [34], The descriptors fall into three categories topological, electronic, and geometric. 

One of the software systems available for pattern recognition studies is ADAPT (automated data analysis using pattern recognition techniques). The structure of each member of the data set is represented by molecular descriptors. These numerical indices, which encode information about the molecule, fall into four classes topological, geometrical, electronic and physicochemical. The data are analysed using pattern recognition techniques to develop a classifier which can discriminate between the classes of data. 

For completeness, we mention that there are several other methodologies for the characterization of the shape of molecular surfaces from local geometric features. The use of Fourier shape descriptors is an interesting alternative, adapted recently for the analysis of macromolecular surfaces. 

Local geometrical features are essential for understanding binding properties, catalytic behavior, and molecular recognition. Many of the descriptors used for global analysis can be adapted to study local features. For instance, mean and Gaussian curvature distributions of a surface, curvature and torsion of a molecular space curves, and the variation of the fractal index Df(r) over a molecular model serve this purpose. 

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