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A program which utilizes the techniques of Artificial Intelligence and Expert Systems to solve problems in the area of Conformational Analysis is described. The program searches conformational space in a systematic fashion, based on the technique known as heuristic state-space search. The program proceeds by recognizing conformational units, assigning one or more conformational templates to each unit, and joining them to form conformational suggestions. These suggestions are criticized to discover logical inconsistencies, and any resulting stresses are resolved. The resulting conformational suggestions are sometimes accurate enough for immediate use, or may be further refined by a numerical program. The latter combination is shown to be quite efficient compared to purely numerical conformational search techniques.

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Many methodologies for performing automated conformational analysis require some means of "perceiving" a molecule to determine features of interest. Algorithms for finding rings, bond orders, and stereocenters and detecting the presence of substructural fragments have been developed. These algorithms are described, emphasizing their importance in conformational analysis.

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An algorithm for predicting the conformations of strained molecules using Artificial Intelligence techniques is described and illustrated with some typical examples.

The results of a wide-ranging investigation into some of the different methods available for performing the 'joining' of templates to build molecular models show that the choice of algorithm can significantly affect the quality of the results obtained, and different algorithms are most suited to particular categories of join.

N-Acetylneuraminic acid (1) is a common sugar in many biological recognition processes. Neuraminidase enzymes recognize and cleave terminal sialic acids from cell surfaces. Viral entry into host cells requires neuraminidase activity, thus inhibition of neuraminidase is a useful strategy for development of drugs for viral infections. A recent crystal structure for influenza viral neuraminidase with sialic acid bound shows that the sialic acid is in a boat conformation [Prot Struct Funct Genet 14: 327 (1992)]. Our studies seek to determine if structural pre-organization can be achieved through the use of sialyllactones. Determination of whether siallylactones are pre-organized in a binding conformation requires conformational analysis. Our inability to find a systematic study comparing the results obtained by various computational methods for carbohydrate modeling led us to compare two different conformational analysis techniques, four different force fields, and three different solvent models. The computational models were compared based on their ability to reproduce experimental coupling constants for sialic acid, sialyl-1,4-lactone, and sialyl-1,7-lactone derivatives. This study has shown that the MM3 forcefield using the implicit solvent model for water implemented in Macromodel best reproduces the experimental coupling constants. The low-energy conformations generated by this combination of computational methods are pre-organized toward conformations which fit well into the active site of neuraminidase.