title: Ligand-based virtual screening using Bayesian inference network and reweighted fragments.
abstract: Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, 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. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.
authors.affiliation: Faculty of Computer Science and Information Systems, Universiti Tecknologi Malaysia, Skudai, Malaysia. alikarary@gmail.com
authors.forename: Ali
authors.initials: A
authors.lastname: Ahmed
authors.forename: Ammar
authors.initials: A
authors.lastname: Abdo
authors.forename: Naomie
authors.initials: N
authors.lastname: Salim
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008024:Ligands; D055641:Mathematical Concepts; D004364:Pharmaceutical Preparations / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship
chemical_list: D008024:Ligands; D004364:Pharmaceutical Preparations