Articles producció científica> Enginyeria Informàtica i Matemàtiques

Ligand-based virtual screening based on the graph edit distance

  • Dades identificatives

    Identificador: imarina:9242251
    Autors:
    Rica, ElenaAlvarez, SusanaSerratosa, Francesc
    Resum:
    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.
  • Altres:

    Autor segons l'article: Rica, Elena; Alvarez, Susana; Serratosa, Francesc
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Alvarez Fernandez, Susana Maria / Rica Alarcón, María Elena / Serratosa Casanelles, Francesc d'Assís
    Paraules clau: 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
    Resum: 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.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: mariaelena.rica@estudiants.urv.cat mariaelena.rica@estudiants.urv.cat susana.alvarez@urv.cat francesc.serratosa@urv.cat
    Identificador de l'autor: 0000-0002-1376-2034 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.mdpi.com/1422-0067/22/23/12751
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: International Journal Of Molecular Sciences. 22 (23): 12751-
    Referència de l'ítem segons les normes 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
    DOI de l'article: 10.3390/ijms222312751
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2021
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Biochemistry & Molecular Biology,Catalysis,Chemistry, Multidisciplinary,Computer Science Applications,Inorganic Chemistry,Medicine (Miscellaneous),Molecular Biology,Organic Chemistry,Physical and Theoretical Chemistry,Spectroscopy
    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
    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
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