ArticleNature communications2021
Inferring experimental procedures from text-based representations of chemical reactions.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 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
25 citing papers in PubMed.
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- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis.Chemical science · 2025Article
- From "Information" to Configuration and Meaning: In Living Systems, the Structure Is the Function.International journal of molecular sciences · 2025Review
- Intelligent Systems for Inorganic Nanomaterial Synthesis.Nanomaterials (Basel, Switzerland) · 2025Review
- Artificial intelligence in drug development.Nature medicine · 2025Review
- Language models can identify enzymatic binding sites in protein sequences.Computational and structural biotechnology journal · 2024Article
- Leveraging infrared spectroscopy for automated structure elucidation.Communications chemistry · 2024Article
- Deep learning in template-free de novo biosynthetic pathway design of natural products.Briefings in bioinformatics · 2024Review
- GEMTELLIGENCE: Accelerating gemstone classification with deep learning.Communications engineering · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
- Augmenting large language models with chemistry tools.Nature machine intelligence · 2024Article
- Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis.Nature communications · 2023Article
- Fast Customization of Chemical Language Models to Out-of-Distribution Data Sets.Chemistry of materials : a publication of the American Chemical Society · 2023Article
- Unbiasing Retrosynthesis Language Models with Disconnection Prompts.ACS central science · 2023Article
- Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language.Nature communications · 2023Article
- Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions.Digital discovery · 2023Article
- Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning.Digital discovery · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental protocols. Although data-driven schemes, such as retrosynthetic models, are becoming established technologies in synthetic organic chemistry, the conversion of proposed synthetic routes to experimental procedures remains a burden on the shoulder of domain experts. In this work, we present data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry. We generated a data set of 693,517 chemical equations and associated action sequences by extracting and processing experimental procedure text from patents, using state-of-the-art natural language models. We used the attained data set to train three different models: a nearest-neighbor model based on recently-introduced reaction fingerprints, and two deep-learning sequence-to-sequence models based on the Transformer and BART architectures. An analysis by a trained chemist revealed that the predicted action sequences are adequate for execution without human intervention in more than 50% of the cases.
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.