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Computational Methods for Epigenetic Drug Discovery: A Focus on Activity Landscape Modeling
J Jesús Naveja1, C Iluhí Oviedo-Osornio2, José L Medina-Franco2
1Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico; PECEM, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Advances in Protein Chemistry and Structural Biology
|August 29, 2018
Summary
This study analyzes structure-activity relationships (SARs) for epigenetic drug targets using activity landscape modeling. It identifies targets with easier or harder drug discovery paths and specific structural changes influencing biological activity.
Area of Science:
- Pharmacology
- Medicinal Chemistry
- Computational Biology
Background:
- Epigenetic drug discovery is a growing field for treating complex diseases.
- Large databases of structure-epigenetic activity relationships are available for analysis.
- Understanding structure-activity relationships (SARs) is crucial for drug development.
Purpose of the Study:
- To perform a large-scale analysis of SARs for epigenetic targets.
- To utilize activity landscape modeling for a novel visual representation of SARs.
- To identify epigenetic targets with predictable or challenging drug-discovery trajectories.
Main Methods:
- Large-scale quantitative analysis of SAR data for epigenetic targets.
- Application of activity landscape modeling to visualize SAR patterns.
- Identification of continuous and discontinuous SAR regions within the epigenetic landscape.
Main Results:
- The study identified regions of epigenetic targets with continuous and discontinuous SAR.
- It highlighted specific structural modifications linked to significant shifts in biological activity.
- Epigenetic targets were categorized based on the anticipated ease or difficulty of drug discovery.
Conclusions:
- This work provides the most comprehensive SAR analysis of multiple epigenetic targets to date.
- Activity landscape modeling offers valuable insights into epigenetic drug discovery.
- The findings aid in prioritizing targets and understanding SAR complexities for future drug development.
Keywords:
Activity cliffsChemical spaceChemoinformaticsDockingEpi-informaticsSALISAS mapsStructure–activity relationshipsMore Related Videos
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