ArticleACS central science2025
Generative Deep Learning-Based Efficient Design of Organic Molecules with Tailored Properties.
Article in ACS central science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Multiobjective Fluorescent Molecule Design with a Data-Physics Dual-Driven Generative Framework.JACS Au · 2026Article
- Interpretable Deep Learning of Molecule-Solvent Interactions Using a Solvatochromic Subgroup Contribution Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modular Framework for 3D Molecular Generation in Computational Chemistry Applications.Journal of the American Chemical Society · 2026Article
- AI-Driven Acceleration of Fluorescence Probe Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A modular artificial intelligence framework to facilitate fluorophore design.Nature communications · 2025Article
- Recent Advances in Probing Electron Delocalization in Conjugated Molecules by Attached Infrared Reporter Groups for Energy Conversion and Storage.ACS applied energy materials · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Innovative approaches to design molecules with tailored properties are required in various research areas. Deep learning methods can accelerate the discovery of new materials by leveraging molecular structure-property relationships. In this study, we successfully developed a generative deep learning (Gen-DL) model that was trained on a large experimental database (DB
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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.