Fragment Reweighting in Ligand-Based Virtual Screening

Many methods have developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have introduced, Tanimoto coefficient being the most prominent. Many approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. In this paper we enhance the effectiveness of Tanimoto and Bayesian inference methods for compound retrieval by using reweighted fragment. Here, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. At the end, our simulated virtual screening experiments with MDDR data sets showed that this approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought have a high degree of structural heterogeneity.