ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening

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Authors

SABANDO María Virginia ULBRICH Pavol SELZER Matías BYŠKA Jan MIČAN Jan PONZONI Ignacio SOTO Axel J. GANUZA María Luján KOZLÍKOVÁ Barbora

Year of publication 2021
Type Article in Periodical
Magazine / Source IEEE Transactions on Visualization and Computer Graphics
MU Faculty or unit

Faculty of Informatics

Citation
Web https://ieeexplore.ieee.org/document/9222282
Doi http://dx.doi.org/10.1109/TVCG.2020.3030438
Keywords Virtual screening;visual analysis;dimensionality reduction;coordinated views;cheminformatics
Description In the modern drug discovery process, medicinal chemists deal with the complexity of analysis of large ensembles of candidate molecules. Computational tools, such as dimensionality reduction (DR) and classification, are commonly used to efficiently process the multidimensional space of features. These underlying calculations often hinder interpretability of results and prevent experts from assessing the impact of individual molecular features on the resulting representations. To provide a solution for scrutinizing such complex data, we introduce ChemVA, an interactive application for the visual exploration of large molecular ensembles and their features. Our tool consists of multiple coordinated views: Hexagonal view, Detail view, 3D view, Table view, and a newly proposed Difference view designed for the comparison of DR projections. These views display DR projections combined with biological activity,selected molecular features, and confidence scores for each of these projections. This conjunction of views allows the user to drill down through the dataset and to efficiently select candidate compounds. Our approach was evaluated on two case studies of finding structurally similar ligands with similar binding affinity to a target protein, as well as on an external qualitative evaluation. The results suggest that our system allows effective visual inspection and comparison of different high-dimensional molecular representations.Furthermore, ChemVA assists in the identification of candidate compounds while providing information on the certainty behind different molecular representations.
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