ArticleJournal of the American Chemical Society2025
Bayesian Optimization for Multicomponent Supramolecular Systems.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Review
- AI-enabled, QbD-aligned Predictive, and Sustainable Design of Natural Polymer-based Drug Delivery Systems.AAPS PharmSciTech · 2026Review
- PhaseTransfer: A transfer learning framework for efficient phase diagram mapping.npj computational materials · 2026Article
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- Evolving Data-Driven Strategies for the Characterization of Supramolecular Polymers and Systems.Angewandte Chemie (International ed. in English) · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diversity of noncovalent interactions makes the design space of multicomponent molecular systems highly complex. To efficiently explore supramolecular design space, data-driven strategies are needed. Here, we demonstrate a methodological framework for the targeted design of multicomponent molecular systems with noncovalent interactions using Bayesian optimization. Its effective applicability to supramolecular polymers is illustrated by three representative cases that reveal accelerated exploration of diverse multicomponent systems with a universal Bayesian optimization framework. The number of experiments required to arrive at optimal compositions is significantly reduced compared to random or uninformed sampling strategies, enabling the experimental study of high-dimensional design spaces. In this way, we can tune the formulation of intricate mixtures and achieve tailored macroscopic properties with minimal experimental effort. Our results show that Bayesian optimization is a general tool for developing multicomponent supramolecular systems with designed functionality.
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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.