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

Authors :
Javed Iqbal
Martin Vogt
Jürgen Bajorath
Source :
Molecules, Vol 25, Iss 17, p 3952 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

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.

Details

Language :
English
ISSN :
25173952 and 14203049
Volume :
25
Issue :
17
Database :
Directory of Open Access Journals
Journal :
Molecules
Publication Type :
Academic Journal
Accession number :
edsdoj.bae3dec9d10b49d6ab1d080a53d2c80e
Document Type :
article
Full Text :
https://doi.org/10.3390/molecules25173952