Autor segons l'article: Garcia-Hernandez, Carlos; Fernandez, Alberto; Serratosa, Francesc
Departament: Enginyeria Informàtica i Matemàtiques Enginyeria Química
Autor/s de la URV: Fernández Sabater, Alberto / Serratosa Casanelles, Francesc d'Assís
Codi de projecte: Grant agreement No. 713679
Paraules clau: Virtual screening Structure-activity relationships Molecular similarity Machine learning Ligands Learning Graph edit distance Extended reduced graph Drug evaluation, preclinical Databases, factual Computer graphics structure-activity relationships molecular similarity machine learning graph edit distance extended reduced graph
Resum: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem.This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance.Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used.In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS.This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Àrees temàtiques: Saúde coletiva Química Psicología Planejamento urbano e regional / demografia Odontología Medicine (miscellaneous) Medicina iii Medicina ii Medicina i Interdisciplinar General medicine Farmacia Ensino Educação física Drug discovery Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Chemistry, medicinal Biotecnología Biodiversidade Astronomia / física
Adreça de correu electrònic de l'autor: alberto.fernandez@urv.cat francesc.serratosa@urv.cat
Identificador de l'autor: 0000-0002-1241-1646 0000-0001-6112-5913
Data d'alta del registre: 2024-10-12
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.eurekaselect.com/182468/article
Programa de finançament: : Marie Skłodowska-Curie Actions - European Union's Horizon 2020 research and innovation programme
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Current Topics In Medicinal Chemistry. 20 (18): 1582-1592
Referència de l'ítem segons les normes APA: Garcia-Hernandez, Carlos; Fernandez, Alberto; Serratosa, Francesc (2020). Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening. Current Topics In Medicinal Chemistry, 20(18), 1582-1592. DOI: 10.2174/1568026620666200603122000
Acrònim: MFP
DOI de l'article: 10.2174/1568026620666200603122000
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Acció del programa de finançament: Martí i Franquès COFUND Doctoral Programme
Tipus de publicació: Journal Publications