ArticleChemical science2026
Synthesis and machine learning techniques to enable data-driven investigation of supramolecular host-guest interactions.
Article in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Authors and funding
5 authors.
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Abstract
The availability of large datasets such as the Protein DataBank and ChEMBL have allowed for rapid progress in developing machine learning tools for predicting the biological activity of organic small molecules. The binding between supramolecular hosts and their desired guests is governed by the same forces that drive protein-small molecule interactions, and yet this field has seen dramatically less application of machine learning. In this contribution, we demonstrate that the production of easily diversified building blocks can allow a single laboratory to generate a dataset that is sufficient to engage with modern machine learning approaches. A range of methods were evaluated against our single-laboratory dataset, with a graph neural network featuring an attention mechanism providing meaningful performance in this data-sparse arena.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.