Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow
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| Udgivet i: | Journal of Cheminformatics vol. 16, no. 1 (Dec 2024), p. 101 |
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| Hovedforfatter: | |
| Andre forfattere: | , , , , |
| Udgivet: |
Springer Nature B.V.
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| Online adgang: | Citation/Abstract Full Text - PDF |
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| 100 | 1 | |a Moreira-Filho, José T. |u National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of Translational Toxicology, Research Triangle Park, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) | |
| 245 | 1 | |a Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset.Scientific contributionsThis work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results. | |
| 653 | |a Source code | ||
| 653 | |a Physicochemical properties | ||
| 653 | |a Applications programs | ||
| 653 | |a Skills | ||
| 653 | |a Workflow | ||
| 653 | |a Unsupervised learning | ||
| 653 | |a Biological activity | ||
| 653 | |a Feature selection | ||
| 653 | |a Biological properties | ||
| 653 | |a Machine learning | ||
| 653 | |a User interfaces | ||
| 653 | |a Automation | ||
| 653 | |a Irritation | ||
| 653 | |a Natural language (computers) | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Natural language | ||
| 653 | |a Data analysis | ||
| 653 | |a Informatics | ||
| 653 | |a Chemical activity | ||
| 653 | |a Corrosion mechanisms | ||
| 653 | |a Summaries | ||
| 653 | |a Algorithms | ||
| 653 | |a Corrosion tests | ||
| 653 | |a Software | ||
| 653 | |a Visualization | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Ranganath, Dhruv |u University of North Carolina at Chapel Hill, Chapel Hill, USA (GRID:grid.10698.36) (ISNI:0000 0001 2248 3208) | |
| 700 | 1 | |a Conway, Mike |u National Institute of Environmental Health Sciences, Research Triangle Park, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) | |
| 700 | 1 | |a Schmitt, Charles |u National Institute of Environmental Health Sciences, Division of Translational Toxicology, Research Triangle Park, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) | |
| 700 | 1 | |a Kleinstreuer, Nicole |u National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of Translational Toxicology, Research Triangle Park, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) | |
| 700 | 1 | |a Mansouri, Kamel |u National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of Translational Toxicology, Research Triangle Park, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) | |
| 773 | 0 | |t Journal of Cheminformatics |g vol. 16, no. 1 (Dec 2024), p. 101 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3093686877/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3093686877/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |