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Quantitative Structure-Epigenetic Activity Relationships

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

Abstract

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 .

Keywords

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

List of abbreviations

ACG

Activity Cliffs Generators

ALM

Activity Landscape Modeling

BRD

Bromodomain

DNMT

DNA Methyltransferase

ERCS

Epigenetic Relevant Chemical Space

FDA

Food and Drug Administration

HDAC

Histone lysine Deacetylase

ISMs

Instances that should be Misclassified

MMP

Matched Molecular Pairs

MODI

Modelability Index

NSG

Network-like Similarity Graphs

PLIF

Protein-Ligand Interaction Fingerprint

PLM

Property Landscape Modeling

QSAR

Quantitative Structure-Activity Relationship

SALI

Structure Activity Landscape Index

SAR

Structure-Activity Relationship

SAS

Structure-Activity Similarity

SEARSSEARS

Structure-Epigenetic Activity Relationships

SARI

Structure-Activity Relationship Index

SmAR

Structure-multiple Activity Relationship

SVMs

Support Vector Machines

Notes

Acknowledgements

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
  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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