ReviewJournal of chemical information and modeling2022
Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.
Review in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- Investigating the anticancer activity of eravacycline in pancreatic cancer via target-based deep learning and experimental validation.Briefings in bioinformatics · 2026Article
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Mechanism-informed rules tunably balance novelty and feasibility of predicted enzymatic reactions.bioRxiv : the preprint server for biology · 2026Article
- SynRXN: An Open Benchmark and Curated Dataset for Computational Reaction Modeling.Scientific data · 2026Article
- Article
- ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models.Journal of chemical information and modeling · 2026Article
- Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next.ACS engineering Au · 2026Review
- A disease potential-driven graph attention model for comorbidity risk prediction of hypertension.Frontiers in big data · 2026Article
- GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networks.Digital discovery · 2025Article
- Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states.Digital discovery · 2025Article
- Graph neural networks for predicting metal-ligand coordination of transition metal complexes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions.Journal of chemical information and modeling · 2025Article
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- RC-GNN: A predictive model of enzyme-reaction pairs.bioRxiv : the preprint server for biology · 2025Article
- Developing Pharmaceutically Relevant Pd-Catalyzed C-N Coupling Reactivity Models Leveraging High-Throughput Experimentation.Journal of the American Chemical Society · 2025Article
- Predicting three-component reaction outcomes from ~40,000 miniaturized reactant combinations.Science advances · 2025Article
- Machine learning applications for thermochemical and kinetic property prediction.Reviews in chemical engineering · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust, and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task-dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions, and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields, or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark data sets for future work. All data sets and the developed reaction GCNN model are available online, free of charge, and open source.
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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.