Ligand-Based Virtual Screening Based on the Graph Edit Distance

Abstract

Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these components. In this case, pharmacophore-type node descriptions are represented by nodes and chemical bounds by edges. If we want to obtain the bioactivity dissimilarity between two chemical compounds, a distance between attributed graphs can be used. The Graph Edit Distance allows computing this distance, and it is defined as the cost of transforming one graph into another. Nevertheless, to define this dissimilarity, the transformation cost must be properly tuned. The aim of this paper is to analyse the structural-based screening methods to verify the quality of the Harper transformation costs proposal and to present an algorithm to learn these transformation costs such that the bioactivity dissimilarity is properly defined in a ligand-based virtual screening application. The goodness of the dissimilarity is represented by the classification accuracy. Six publicly available datasets—CAPST, DUD-E, GLL&GDD, NRLiSt-BDB, MUV and ULS-UDS—have been used to validate our methodology and show that with our learned costs, we obtain the highest ratios in identifying the bioactivity similarity in a structurally diverse group of molecules.

ATENA: A Web-Based Tool for Modelling Metal Oxide Nanoparticles Based on NanoFingerprint Quantitative Structure–Activity Relationships

“…The maximum number of bonds was 10 (there are not atoms with more bonds in this compound). (10). The next two elements of the GraphFingerprint concern the elements being oxygen (atomic number: 8) and aluminium (atomic number: 13).…”

“…The three-dimensional structures of nanoparticles can be represented as attributed graphs [10]. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges.…”

“…Nodes are individual components in the object, and edges are relationships between these components. Attributed graphs have been applied in machine learning for a long time in applications ranging from character recognition to social network exploring, among others, and more precisely, in Ligand-Based Virtual Screening [8,10,11]. In our case, atoms in the nanoparticle are represented by nodes, and chemical bounds, by edges.…”

“…(b) NanoFingerprint of chemical nanocompound Al 2 O 3 with a size of 13Angstroms. The first element of the NanoFingerprint concerns the maximum number of edges(10). The next two elements of the GraphFingerprint concern the elements being oxygen (atomic number: 8) and aluminium (atomic number: 13).…”

Classification and Recognition of Bilingual Text Using Graph Edit Distance Based Degree of Similarity

“…In the literature, various approaches have been proposed for graph matching problems. Most graph matching algorithms are based on the maximum common subgraph problem, and it is a non-deterministic polynomial-time complexity problem (7,8) . In the decision tree approach (9) , model graphs are represented by decision trees.…”