ArticleScientific reports2021
Discovery of novel chemical reactions by deep generative recurrent neural network.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- RXNMapper-v2: updated validation and evaluation on expanded chemical space.Digital discovery · 2026Article
- Developing muscarinic receptor M1 classification models utilizing transfer learning and generative AI techniques.Scientific reports · 2025Article
- Challenging Reaction Prediction Models to Generalize to Novel Chemistry.ACS central science · 2025Article
- From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.Molecular informatics · 2025Article
- Implementation of a soft grading system for chemistry in a Moodle plugin: reaction handling.Journal of cheminformatics · 2024Article
- Continuous flow synthesis enabling reaction discovery.Chemical science · 2024Review
- Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery.Journal of cheminformatics · 2024Article
- Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.Nature reviews. Drug discovery · 2024Review
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Digitizing chemical discovery with a Bayesian explorer for interpreting reactivity data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios.Journal of chemical information and modeling · 2023Article
- LinChemIn: SynGraph-a data model and a toolkit to analyze and compare synthetic routes.Journal of cheminformatics · 2023Article
- Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery.Chemical science · 2023Review
- SelenzymeRF: updated enzyme suggestion software for unbalanced biochemical reactions.Computational and structural biotechnology journal · 2023Article
- Generation of novel Diels-Alder reactions using a generative adversarial network.RSC advances · 2022Article
- SELFIES and the future of molecular string representations.Patterns (New York, N.Y.) · 2022Review
- Improving Chemical Reaction Prediction with Unlabeled Data.Molecules (Basel, Switzerland) · 2022Article
- From theory to experiment: transformer-based generation enables rapid discovery of novel reactions.Journal of cheminformatics · 2022Article
- Machine Learning Applications for Chemical Reactions.Chemistry, an Asian journal · 2022Review
- Exploring Chemical Reaction Space with Reaction Difference Fingerprints and Parametric t-SNE.ACS omega · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The "creativity" of Artificial Intelligence (AI) in terms of generating de novo molecular structures opened a novel paradigm in compound design, weaknesses (stability & feasibility issues of such structures) notwithstanding. Here we show that "creative" AI may be as successfully taught to enumerate novel chemical reactions that are stoichiometrically coherent. Furthermore, when coupled to reaction space cartography, de novo reaction design may be focused on the desired reaction class. A sequence-to-sequence autoencoder with bidirectional Long Short-Term Memory layers was trained on on-purpose developed "SMILES/CGR" strings, encoding reactions of the USPTO database. The autoencoder latent space was visualized on a generative topographic map. Novel latent space points were sampled around a map area populated by Suzuki reactions and decoded to corresponding reactions. These can be critically analyzed by the expert, cleaned of irrelevant functional groups and eventually experimentally attempted, herewith enlarging the synthetic purpose of popular synthetic pathways.
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.