Several different QSAR techniques have been applied to sweetness data for 50 sulfamates, RNHSO3Na (21 sweet, 20 sweet-bitter, and 9 bitter). Stepwise discriminant analysis has been used to separate the 50 molecules into 3 classes, sweet, sweet-bitter, and bitter. Cluster analysis using two principal components can clearly distinguish between the sweet and sweet-bitter molecules but not between all three classes. Regression analysis has been used to develop equations for parameters fitting to log(RS) (RS, relative sweetness). The genetic algorithm method has been used to select parameters, and high correlations between log(RS) and a range of parameters have been achieved. Molecular field analysis followed by selection of relevant grid points by genetic algorithm yielded a result in which six grid points gave a high correlation coefficient (r 2 = 0.958, XVr 2 = 0.902). Keywords: Sweetness; QSAR; sulfamates; bitterness

The traditional method of analyzing solution structuring properties of solutes using atom–atom radial distribution functions (rdfs) can give rise to misleading interpretations when the volume occupied by the solute is ignored. It is shown by using the examples of O(4) in α‐ and β‐D‐allose that a more reliable interpretation of rdfs can be obtained by normalising the rdf using the available volume, rather than the traditional volume of a spherical shell. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 363–367, 1998

A practical and pragmatic method is demonstrated that aligns lead-like properties with compound diversity for the picking of compounds to synthesise from large virtual libraries. Methods are highlighted for decreasing synthetic attrition through the prior filtration of reagents sets grouped by reaction type. Also disclosed are protocols that use a combination of predicted physicochemical parameters and potential toxicological liabilities to enable the synthesis of lead-like compounds with a low potential risk of exhibiting toxicity or undesirable physicochemical properties. Lastly, a compound-picking process for a 2D compound matrix is demonstrated that maximises the diversity coverage whilst minimising synthetic effort. Thus a very highly optimised process is shown that delivers premium sample quality where lead-likeness and novelty are aligned to afford the best possible enhancement for the corporate compound collection.

Isovanillyl derivatives constitute a large class of sweet compounds in which there is a high degree of structural similarity and a wide range of biological activity, the relative sweetness RS spanning from 50 to 10 000 times with respect to sucrose. This paper describes the results obtained by applying statistical models to develop QSARs for these derivatives. For a set of 14 compounds (set 1) appropriate physicochemical parameters for regression equations were selected using the genetic algorithm method. The best equation indicates a very close correlation (N 14, ND 5, r 2 0.982, r 2 cv 0:942, LOF 0.074, PRESS 0.271, S PRESS 0.184, S DEP 0.139). Good results have also been obtained by Molecular Field Analysis (MFA) applied to the same set of compounds (N 14, ND 4, r 2 0.957, r 2 cv 0:925, LOF 0.044, PRESS 0.348, S PRESS 0.196, S DEP 0.158). QSARs have also been derived for a larger set of 41 compounds (set 2, including set 1, plus other 27 compounds) with a much larger variety of structural types. These compounds have been divided into a training set of 35 compounds and a test set of 6 compounds. The most signi®cant QSAR obtained using physicochemical parameters (N 35, ND 6, r 2 0.673, r 2 cv 0:522, LOF 0.337, PRESS 7.432, S PRESS 0.515, S DEP 0.461) proved less successful than one using MFA parameters (N 35, ND 6, r 2 0.746, r 2 cv 0:607, LOF 0.261, PRESS 6.110, S PRESS 0.467, S DEP 0.418). PRESS values for the test set were 4.079 and 1.962 respectively showing that the MFA data had more predictive power.Equations with different numbers of descriptors were compared and it was concluded that the LOF which is dependent upon the number of parameters used as well as the sum of squares is a suitable measure of equation quality. These equations were also validated by scrambling the experimental data which gave signi®cantly worse agreement than the real data except when an excessive number of descriptors was used.