Cargando…

Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data

Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representat...

Descripción completa

Detalles Bibliográficos
Autores principales: Iqbal, Javed, Vogt, Martin, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7504767/
https://www.ncbi.nlm.nih.gov/pubmed/32872506
http://dx.doi.org/10.3390/molecules25173952
_version_ 1783584699637039104
author Iqbal, Javed
Vogt, Martin
Bajorath, Jürgen
author_facet Iqbal, Javed
Vogt, Martin
Bajorath, Jürgen
author_sort Iqbal, Javed
collection PubMed
description Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representations have been introduced. For SAR analysis, 3D AL models are particularly intuitive. In these models, an interpolated potency surface is added as a third dimension to a 2D projection of chemical space. Accordingly, AL topology can be associated with characteristic SAR features. Going beyond visualization and a qualitative assessment of SARs, it would be very helpful to compare 3D ALs of different datasets in more quantitative terms. However, quantitative AL analysis is still in its infancy. Recently, it has been shown that 3D AL models with pre-defined topologies can be correctly classified using machine learning. Classification was facilitated on the basis of AL image feature representations learned with convolutional neural networks. Therefore, we have further investigated image analysis for quantitative comparison of 3D ALs and devised an approach to determine (dis)similarity relationships for ALs representing different compound datasets. Herein, we report this approach and demonstrate proof-of-principle. The methodology makes it possible to computationally compare 3D ALs and quantify topological differences reflecting varying SAR information content. For SAR exploration in drug design, this adds a quantitative measure of AL (dis)similarity to graphical analysis.
format Online
Article
Text
id pubmed-7504767
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75047672020-09-26 Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data Iqbal, Javed Vogt, Martin Bajorath, Jürgen Molecules Article Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representations have been introduced. For SAR analysis, 3D AL models are particularly intuitive. In these models, an interpolated potency surface is added as a third dimension to a 2D projection of chemical space. Accordingly, AL topology can be associated with characteristic SAR features. Going beyond visualization and a qualitative assessment of SARs, it would be very helpful to compare 3D ALs of different datasets in more quantitative terms. However, quantitative AL analysis is still in its infancy. Recently, it has been shown that 3D AL models with pre-defined topologies can be correctly classified using machine learning. Classification was facilitated on the basis of AL image feature representations learned with convolutional neural networks. Therefore, we have further investigated image analysis for quantitative comparison of 3D ALs and devised an approach to determine (dis)similarity relationships for ALs representing different compound datasets. Herein, we report this approach and demonstrate proof-of-principle. The methodology makes it possible to computationally compare 3D ALs and quantify topological differences reflecting varying SAR information content. For SAR exploration in drug design, this adds a quantitative measure of AL (dis)similarity to graphical analysis. MDPI 2020-08-29 /pmc/articles/PMC7504767/ /pubmed/32872506 http://dx.doi.org/10.3390/molecules25173952 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Iqbal, Javed
Vogt, Martin
Bajorath, Jürgen
Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data
title Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data
title_full Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data
title_fullStr Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data
title_full_unstemmed Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data
title_short Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data
title_sort computational method for quantitative comparison of activity landscapes on the basis of image data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7504767/
https://www.ncbi.nlm.nih.gov/pubmed/32872506
http://dx.doi.org/10.3390/molecules25173952
work_keys_str_mv AT iqbaljaved computationalmethodforquantitativecomparisonofactivitylandscapesonthebasisofimagedata
AT vogtmartin computationalmethodforquantitativecomparisonofactivitylandscapesonthebasisofimagedata
AT bajorathjurgen computationalmethodforquantitativecomparisonofactivitylandscapesonthebasisofimagedata