ArticleChemical science2020
BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Chemically Interpretable Prediction Models for Silicon and Germanium Bond Dissociation Enthalpies.ACS omega · 2026Article
- Atomistic machine learning with irreducible Cartesian natural tensors.Nature communications · 2026Article
- Chemical intuition on bond-dissociation energies as an emergent ability of universal machine-learning interatomic potentials.Nature communications · 2026Article
- A crystal graph to vector approach for predicting magnetic properties.Scientific reports · 2026Article
- Multi-level QTAIM-enriched graph neural networks for resolving properties of transition metal complexes.Digital discovery · 2025Article
- Smart Reaction Templating: A Graph-Based Method for Automated Molecular Dynamics Input Generation.Journal of chemical information and modeling · 2025Article
- Machine learning applications for thermochemical and kinetic property prediction.Reviews in chemical engineering · 2025Review
- HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions.Journal of chemical information and modeling · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Transformer-generated atomic embeddings to enhance prediction accuracy of crystal properties with machine learning.Nature communications · 2025Article
- Molecular Merged Hypergraph Neural Network for Explainable Solvation Gibbs Free Energy Prediction.Research (Washington, D.C.) · 2025Article
- Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials.iScience · 2024Article
- Designing solvent systems using self-evolving solubility databases and graph neural networks.Chemical science · 2024Article
- Predicting bond dissociation energies of cyclic hypervalent halogen reagents using DFT calculations and graph attention network model.Beilstein journal of organic chemistry · 2024Article
- Comparative Evaluation of Chemical and Photolytic Denitrosation Methods for Chemiluminescence Detection of TotalEnvironmental science & technology · 2023Article
- Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks.The journal of physical chemistry. A · 2023Article
- PubChem 2023 update.Nucleic acids research · 2023Article
- Machine Learning to Predict Homolytic Dissociation Energies of C-H Bonds: Calibration of DFT-based Models with Experimental Data.Molecular informatics · 2023Article
- Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining.Chemical science · 2022Article
- Graph neural networks for materials science and chemistry.Communications materials · 2022Review
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
A broad collection of technologies, including
Identifiers
What OpenQuestion holds
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.