Computing wiener-type indices for virtual combinatorial libraries generated from heteroatom-containing building blocks.

The expensive and time-consuming process of drug lead discovery is significantly accelerated by efficiently screening molecular libraries with a high structural diversity and selecting subsets of molecules according to their similarity toward specific collections of active compounds. To characterize the molecular similarity/diversity or to quantify the drug-like character of compounds the process of screening virtual and synthetic combinatorial libraries uses various classes of structural descriptors, such as structure keys, fingerprints, graph invariants, and various topological indices computed from atomic connectivities or graph distances. In this paper we present efficient algorithms for the computation of several distance-based topological indices of a molecular graph from the distance invariants of its subgraphs. The procedures utilize vertex- and edge-weighted molecular graphs representing organic compounds containing heteroatoms and multiple bonds. These equations offer an effective way to compute for weighted molecular graphs the Wiener index, even/odd Wiener index, and resistance-distance index. The proposed algorithms are especially efficient in computing distance-based structural descriptors in combinatorial libraries without actually generating the compounds, because only distance-based indices of the building blocks are needed to generate the topological indices of any compound assembled from the building blocks.