Ligand-based virtual screening under partial shape constraints

Ligand-based virtual screening has proven to be a viable technology during the search for new lead structures in drug discovery. Despite the rapidly increasing number of published methods, meaningful shape matching as well as ligand and target flexibility still remain open challenges. In this work, we analyze the influence of knowledge-based sterical constraints on the performance of the recently published ligand-based virtual screening method mRAISE. We introduce the concept of partial shape matching enabling a more differentiated view on chemical structure. The new method is integrated into the LBVS tool mRAISE providing multiple options for such constraints. The applied constraints can either be derived automatically from a protein-ligand complex structure or by manual selection of ligand atoms. In this way, the descriptor directly encodes the fit of a ligand into the binding site. Furthermore, the conservation of close contacts between the binding site surface and the query ligand can be enforced. We validated our new method on the DUD and DUD-E datasets. Although the statistical performance remains on the same level, detailed analysis reveal that for certain and especially very flexible targets a significant improvement can be achieved. This is further highlighted looking at the quality of calculated molecular alignments using the recently introduced mRAISE dataset. The new partial shape constraints improved the overall quality of molecular alignments especially for difficult targets with highly flexible or different sized molecules. The software tool mRAISE is freely available on Linux operating systems for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise ).