2021
ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening
SABANDO, María Virginia, Pavol ULBRICH, Matías SELZER, Jan BYŠKA, Jan MIČAN et. al.Základní údaje
Originální název
ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening
Autoři
SABANDO, María Virginia (32 Argentina), Pavol ULBRICH (703 Slovensko, domácí), Matías SELZER (32 Argentina), Jan BYŠKA (203 Česká republika, domácí), Jan MIČAN (203 Česká republika, domácí), Ignacio PONZONI (32 Argentina), Axel J. SOTO (32 Argentina), María Luján GANUZA (32 Argentina) a Barbora KOZLÍKOVÁ (203 Česká republika, garant, domácí)
Vydání
IEEE Transactions on Visualization and Computer Graphics, IEEE, 2021, 1077-2626
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 5.226
Kód RIV
RIV/00216224:14330/21:00118764
Organizační jednotka
Fakulta informatiky
UT WoS
000706330100074
Klíčová slova anglicky
Virtual screening;visual analysis;dimensionality reduction;coordinated views;cheminformatics
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 24. 7. 2023 09:26, doc. RNDr. Barbora Kozlíková, Ph.D.
Anotace
V originále
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.
Návaznosti
GC18-18647J, projekt VaV |
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MUNI/A/1076/2019, interní kód MU |
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MUNI/A/1411/2019, interní kód MU |
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MUNI/A/1549/2020, interní kód MU |
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