ArticleMolecules (Basel, Switzerland)2023
Sampling and Mapping Chemical Space with Extended Similarity Indices.
Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Undersampling Techniques for Nonlinear Chemical Space Visualization.Molecular informatics · 2026Article
- MetAP DB and Metal-FP: a database and fingerprint framework for advancing metal-based drug discovery.Journal of computer-aided molecular design · 2025Article
- Extended Activity Cliffs-Driven Approaches on Data Splitting for the Study of Bioactivity Machine Learning Predictions.Molecular informatics · 2025Article
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.Critical reviews in toxicology · 2024Review
- Alternative weighting schemes for fine-tuned extended similarity indices.Journal of chemometrics · 2024Article
- Efficient clustering of large molecular libraries.bioRxiv : the preprint server for biology · 2024Article
- iSIM: instant similarity.Digital discovery · 2024Article
- The computationally predicted drug-likeness, pharmacokinetics properties, medicinal chemistry parameters, and toxicity properties ofF1000Research · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Visualization of the chemical space is useful in many aspects of chemistry, including compound library design, diversity analysis, and exploring structure-property relationships, to name a few. Examples of notable research areas where the visualization of chemical space has strong applications are drug discovery and natural product research. However, the sheer volume of even comparatively small sub-sections of chemical space implies that we need to use approximations at the time of navigating through chemical space. ChemMaps is a visualization methodology that approximates the distribution of compounds in large datasets based on the selection of satellite compounds that yield a similar mapping of the whole dataset when principal component analysis on a similarity matrix is performed. Here, we show how the recently proposed extended similarity indices can help find regions that are relevant to sample satellites and reduce the amount of high-dimensional data needed to describe a library's chemical space.
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Registered trials
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