ArticleF1000Research2017
ChemMaps: Towards an approach for visualizing the chemical space based on adaptive satellite compounds.
Article in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Undersampling Techniques for Nonlinear Chemical Space Visualization.Molecular informatics · 2026Article
- Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes.Journal of cheminformatics · 2026Review
- Article
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Alchemical analysis of FDA approved drugs.Digital discovery · 2023Article
- Sampling and Mapping Chemical Space with Extended Similarity Indices.Molecules (Basel, Switzerland) · 2023Article
- Progress on open chemoinformatic tools for expanding and exploring the chemical space.Journal of computer-aided molecular design · 2022Article
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- Cheminformatics to Characterize Pharmacologically Active Natural Products.Biomolecules · 2020Review
- One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome.Journal of cheminformatics · 2020Article
- Finding Constellations in Chemical Space Through Core Analysis.Frontiers in chemistry · 2019Article
- Analysis of a large food chemical database: chemical space, diversity, and complexity.F1000Research · 2018Article
- ChemMaps: Towards an approach for visualizing the chemical space based on adaptive satellite compounds.F1000Research · 2017Article
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2 authors.
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Abstract
We present a novel approach called ChemMaps for visualizing chemical space based on the similarity matrix of compound datasets generated with molecular fingerprints' similarity. The method uses a 'satellites' approach, where satellites are, in principle, molecules whose similarity to the rest of the molecules in the database provides sufficient information for generating a visualization of the chemical space. Such an approach could help make chemical space visualizations more efficient. We hereby describe a proof-of-principle application of the method to various databases that have different diversity measures. Unsurprisingly, we found the method works better with databases that have low 2D diversity. 3D diversity played a secondary role, although it seems to be more relevant as 2D diversity increases. For less diverse datasets, taking as few as 25% satellites seems to be sufficient for a fair depiction of the chemical space. We propose to iteratively increase the satellites number by a factor of 5% relative to the whole database, and stop when the new and the prior chemical space correlate highly. This Research Note represents a first exploratory step, prior to the full application of this method for several datasets.
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