ArticleJournal of computer-aided molecular design2017
Predictive cartography of metal binders using generative topographic mapping.
Article in Journal of computer-aided molecular design, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Evaluation of the Topology Space of DNA-Encoded Libraries.Journal of chemical information and modeling · 2023Article
- Machine learning-based analysis of overall stability constants of metal-ligand complexes.Scientific reports · 2022Article
- Discovery of novel chemical reactions by deep generative recurrent neural network.Scientific reports · 2021Article
- Chemical space exploration guided by deep neural networks.RSC advances · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Generative topographic mapping (GTM) approach is used to visualize the chemical space of organic molecules (L) with respect to binding a wide range of 41 different metal cations (M) and also to build predictive models for stability constants (logK) of 1:1 (M:L) complexes using "density maps," "activity landscapes," and "selectivity landscapes" techniques. A two-dimensional map describing the entire set of 2962 metal binders reveals the selectivity and promiscuity zones with respect to individual metals or groups of metals with similar chemical properties (lanthanides, transition metals, etc). The GTM-based global (for entire set) and local (for selected subsets) models demonstrate a good predictive performance in the cross-validation procedure. It is also shown that the data likelihood could be used as a definition of the applicability domain of GTM-based models. Thus, the GTM approach represents an efficient tool for the predictive cartography of metal binders, which can both visualize their chemical space and predict the affinity profile of metals for new ligands.
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Registered trials
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