MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models

The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a new, ready-to-use, multi-tool, and open-source framework f...

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Veröffentlicht in:Journal of cheminformatics Jg. 16; H. 1; S. 98 - 13
1. Verfasser: Sosnin, Sergey
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 12.08.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Accessibility
Analysis
Applicability domain
Chemical compounds
Chemical space visualization
Chemistry
Chemistry and Materials Science
Chemistry, Organic
Chemoinformatics
Clustering
Computational Biology/Bioinformatics
Computer Applications in Chemistry
Data analysis
Datasets
Demand analysis
Documentation and Information in Chemistry
Graph representations
Improving Reproducibility and Reusability in the Journal of Cheminformatics
Informatics
Methods
Neural networks
Open source software
QSAR/QSPR modelling
Software
Source code
Structure-activity relationships
Structure-activity relationships (Biochemistry)
Theoretical and Computational Chemistry
Visual validation
Visualization
Austria
Chemical space visualization
QSAR/QSPR modelling
Clustering
Chemoinformatics
Applicability domain
Visual validation
ISSN:1758-2946, 1758-2946
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Beschreibung
Zusammenfassung:The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a new, ready-to-use, multi-tool, and open-source framework for visualizing and navigating chemical space. This framework adheres to the low-code/no-code (LCNC) paradigm, providing a KNIME node, a web-based tool, and a Python package, making it accessible to a broad cheminformatics community. The core technique of the MolCompass framework employs a pre-trained parametric t-SNE model. We demonstrate how this framework can be adapted for the visualisation of chemical space and visual validation of binary classification QSAR/QSPR models, revealing their weaknesses and identifying model cliffs. All parts of the framework are publicly available on GitHub, providing accessibility to the broad scientific community. Scientific contribution We provide an open-source, ready-to-use set of tools for the visualization of chemical space. These tools can be insightful for chemists to analyze compound datasets and for the visual validation of QSAR/QSPR models.
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ISSN:1758-2946
1758-2946
DOI:10.1186/s13321-024-00888-z