Molecular complementarity plays a central role in the molecular recognition process. The quantification of this complementarity is thus of major importance in computer-aided drug design (CADD), whether one is attempting to create a pharmacophore, search a database, or derive a quantitative structure activity relationship (QSAR). Molecular similarity indices provide a convenient way in which to undertake such molecular property complementarity calculations. Here, a number of techniques are introduced through which the utility of such calculations may be improved. New “discrete” similarity indices are described, which permit more control over molecular complementarity calculations, including graphical analysis. Methods for the rapid analytical evaluation of molecular electrostatic potential (MEP) and shape are proposed, which greatly improve the similarity optimization performance. Finally, a new way to undertake three dimensional (3D) structure activity calculations using molecular similarity data is presented, which leads to the creation of extremely predictive QSAR models. The applicability of the new tools for pharmacophore elucidation and 3D QSAR generation are verified using a number of different biological test systems.
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