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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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 |
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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 |
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