Author, as appears in the article.: Rica, Elena; Alvarez, Susana; Serratosa, Francesc
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Alvarez Fernandez, Susana Maria / Rica Alarcón, María Elena / Serratosa Casanelles, Francesc d'Assís
Keywords: Virtual screening User-computer interface Structure activity relationships Structure activity relation Pharmacophore Molecular similarity Models, theoretical Machine learning Ligands Graph edit distance Extended reduced graph Computer graphics Biological activity Artificial intelligence Article Algorithms Algorithm validation structure activity relationships sets molecular similarity machine learning graph edit distance extended reduced graph diversity analysis design descriptor computation chemistry chemical-structures
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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Thematic Areas: Zootecnia / recursos pesqueiros Spectroscopy Saúde coletiva Química Psicología Physical and theoretical chemistry Organic chemistry Odontología Nutrição Molecular biology Medicine (miscellaneous) Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Interdisciplinar Inorganic chemistry Geociências Farmacia Engenharias iv Engenharias ii Engenharias i Educação física Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, multidisciplinary Catalysis Biotecnología Biodiversidade Biochemistry & molecular biology Astronomia / física
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: mariaelena.rica@estudiants.urv.cat mariaelena.rica@estudiants.urv.cat susana.alvarez@urv.cat francesc.serratosa@urv.cat
Author identifier: 0000-0002-1376-2034 0000-0001-6112-5913
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/1422-0067/22/23/12751
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: International Journal Of Molecular Sciences. 22 (23): 12751-
APA: Rica, Elena; Alvarez, Susana; Serratosa, Francesc (2021). Ligand-based virtual screening based on the graph edit distance. International Journal Of Molecular Sciences, 22(23), 12751-. DOI: 10.3390/ijms222312751
Article's DOI: 10.3390/ijms222312751
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications