Quantitative Structure-Epigenetic Activity Relationships

  • Mario Omar García-Sánchez
  • Maykel Cruz-Monteagudo
  • José L. Medina-FrancoEmail author
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 24)


The relevance of epigenetic drug discovery has increased during the past few years as revealed by the augmenting number of related publications and the amount of structure-epigenetic activity data in compound databases. This chapter discusses the current status of epigenetic target-based therapies. It is also analyzed the progress of quantitative structure-activity relationship (QSAR) models developed for compound databases screened with epigenetic targets. A special emphasis is made on compounds directed to inhibitors of DNA methyltransferases , one of the first epigenetic target families associated with therapeutic potential. Novel approaches applied to develop models for inhibitors of bromodomains , other epigenetic target families with high relevance in modern drug discovery programs, are also discussed. The chapter analyses epigenetic activity landscape modeling, activity cliffs , and activity cliff generators and their relevance to develop QSAR models. Computational methods applied to elucidate Quantitative Structure-Epigenetic Activity Relationships are in line with the increasing and developing research area of Epi-informatics .


Activity cliffs Activity landscape Bromodomains DNA methyltransferase Epigenetics Epi-informatics HDAC SEARS 

List of abbreviations


Activity Cliffs Generators


Activity Landscape Modeling




DNA Methyltransferase


Epigenetic Relevant Chemical Space


Food and Drug Administration


Histone lysine Deacetylase


Instances that should be Misclassified


Matched Molecular Pairs


Modelability Index


Network-like Similarity Graphs


Protein-Ligand Interaction Fingerprint


Property Landscape Modeling


Quantitative Structure-Activity Relationship


Structure Activity Landscape Index


Structure-Activity Relationship


Structure-Activity Similarity


Structure-Epigenetic Activity Relationships


Structure-Activity Relationship Index


Structure-multiple Activity Relationship


Support Vector Machines



Valuable discussions with Oscar Méndez-Lucio, Eli Fernández-de Gortari,and J. Jesús Naveja are highly acknowledged. We also thank Fernando Prieto-Martínez for preparing the data set of bromodomain inhibitors. This work was supported by the Universidad Nacional Autónoma de México (UNAM), grant Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT) IA204016 and Programa de Apoyo a la Investigación y el Posgrado (PAIP) 50009163, Facultad de Química, UNAM. MC-M acknowledges the postdoctoral grant [SFRH/BPD/90673/2012] financed by the FCT—Fundação para a Ciência e a Tecnologia, Portugal, co-financed by the European Social Fund.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mario Omar García-Sánchez
    • 1
  • Maykel Cruz-Monteagudo
    • 2
    • 3
    • 4
  • José L. Medina-Franco
    • 1
    Email author
  1. 1.Facultad de Química, Departamento de FarmaciaUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  2. 2.Center for Computational Science, University of MiamiMiamiUSA
  3. 3.Faculdade de Ciências, CIQUP/Departamento de Química e BioquímicaUniversidade do PortoPortoPortugal
  4. 4.Facultad de Ciências, REQUIMTE, Departamento de Química e BioquímicaUniversidade do PortoPortoPortugal

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