Molecular complexity analysis of de novo designed ligands

Abstract

The de novo approach to structure-based rational drug design can provide a powerful tool for suggestion of entirely novel potential leads. However, programs for structure generation typically generate large numbers of putative ligands; therefore, various heuristics (such as estimation of binding affinity and synthetic accessibility) have to be adopted to evaluate and prune large answer sets with the goal of suggesting ligands with high binding affinity but low structural complexity. A novel method for complexity analysis is described. This method provides a rapid and effective ranking technique for elimination of structures with complicated molecular motifs. This complexity analysis technique, implemented within the SPROUT de novo design system, is based on the statistical distribution of various cyclic and acyclic topologies and atom substitution patterns in existing drugs or commercially available starting materials. A novel feature of the technique that distinguishes it from other published methods is that the matching takes place at various levels of abstraction, so that it can evaluate complexity scores, even for structures which contain atoms with unspecified atom type, which is sometimes the case with the initial output of de novo structure generation systems.