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The performance of two important 2D and 3D molecular descriptors for rational design to maximize the structural diversity of databases is investigated in this publication. Those methods are based either on a 2D description using a binary fingerprint, which accounts for the absence or presence of molecular fragments, or a 3D description based on the geometry of pharmacophoric features encoded in a fingerprint (pharmacophoric definition triplets, PDTs). Both descriptors in combination with maximum dissimilarity selections, complete linkage hierarchical cluster analysis, or sequential dissimilarity selections were compared to random subsets as reference. This comparison is based on their ability to cover representative biological classes from parent databases (coverage analysis) and the degree of separation between active and inactive compounds for a biological target from hierarchical clustering (cluster separation analysis). While the similarity coefficients (Tanimoto, cosine) show only a minor influence, the number of conformations to generate the 3D PDT fingerprint lead to remarkably different results. PDT fingerprints derived from a lower number of conformers perform significantly better, but they are not comparable to a 2D fingerprint-based design. When 2D and 3D descriptors are combined with weighting factors > 0.5 for 2D fingerprints, a significant improvement of coverage and cluster separation results is observed for a small number of PDT conformers and medium sized subsets. Some combined descriptors outperform 2D fingerprints, but not for all subset populations. Applying sequential dissimilarity selection to PDT descriptors reveals that its performance is dependent on the initial ordering of compounds, while presorting according to 2D fingerprint diversity does not improve results. Finally the relationship between biological activity and similarity was investigated, showing that PDTs quantify smaller structural differences due to the large number of bits in the fingerprint.
Matter et al. (Fri,) studied this question.