Abstract
Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.