ArticleJournal of cheminformatics2023
A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Enzyme association for environmental biotransformation reactions through contrastive learning of reaction center-specific fingerprints.Bioinformatics (Oxford, England) · 2026Article
- ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models.Journal of chemical information and modeling · 2026Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- An active representation learning method for reaction yield prediction with small-scale data.Communications chemistry · 2025Article
- De novo drug design through gradient-based regularized search in information-theoretically controlled latent space.Journal of computer-aided molecular design · 2024Article
- AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry.Journal of cheminformatics · 2024Article
- A green, facile, and practical preparation of capsaicin derivatives with thiourea structure.Scientific reports · 2024Article
- A focus on molecular representation learning for the prediction of chemical properties.Chemical science · 2024Article
- Transfer learning for a foundational chemistry model.Chemical science · 2024Article
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R
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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.