An automated method for exploring targeted substructural diversity within sets of chemical structures.

Practicing medicinal chemists tend to treat a lead compound as an assemblage of its substructural parts. By iteratively confining their synthetic efforts in a localized fashion, they are able to systematically investigate how minor changes in certain portions of the molecule effect the properties of interest in the logical expectation that the observed beneficial changes will be cumulative. One disadvantage to this approach arises when large amounts of structure data begin to accumulate which is often the case in recent times due to such developments as high-throughput screening, virtual screening, and combinatorial chemistry. How then does one interactively mine this diverse data consistent with the desired substructural template, so those desirable structural features can be discovered and interpreted, especially when they may not occur in the most active compounds due to structural deficiencies in other portions of the molecule? In this paper, we present an algorithm to automate this process that has historically been performed in an ad-hoc and manual fashion. Using the proposed method, significantly larger numbers of compounds can be analyzed in this fashion, potentially discovering useful structural feature combinations that would not have otherwise been detected due to the sheer scale of modern structural and biological data collections.