Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure

Bayesian Graph Edit Distance

ÐThis paper describes a novel framework for comparing and matching corrupted relational graphs. The paper develops the idea of edit-distance originally introduced for graph-matching by Sanfeliu and Fu [1]. We show how the Levenshtein distance can be used to model the probability distribution for structural errors in the graph-matching problem. This probability distribution is used to locate mat...

Ranking of generalized fuzzy numbers using distance measure and similarity measure

Approximating Graph Edit Distance Using GNCCP

The graph edit distance (GED) is a flexible and widely used dissimilarity measure between graphs. Computing the GED between two graphs can be performed by solving a quadratic assignment problem (QAP). However, the problem is NP complete hence forbidding the computation of the optimal GED on large graphs. To tackle this drawback, recent heuristics are based on a linear approximation of the initi...

Fusing similarity rankings in ligand-based virtual screening

Data fusion is the name given to a range of methods for combining multiple sources of evidence. This mini-review summarizes the use of one such class of methods for combining the rankings obtained when similarity searching is used for ligand-based virtual screening. Two main approaches are described: similarity fusion involves combining rankings from single searches based on multiple similarity...

Scoring ligand similarity in structure-based virtual screening.

Scoring to identify high-affinity compounds remains a challenge in virtual screening. On one hand, protein-ligand scoring focuses on weighting favorable and unfavorable interactions between the two molecules. Ligand-based scoring, on the other hand, focuses on how well the shape and chemistry of each ligand candidate overlay on a three-dimensional reference ligand. Our hypothesis is that a hybr...