ReviewChemistry (Weinheim an der Bergstrasse, Germany)2023
Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis.
Review in Chemistry (Weinheim an der Bergstrasse, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Bridging Chemical Literature and Molecular Graphs: LLM-Augmented Enantioselectivity Prediction for Iridium-Catalyzed Asymmetric C-H Borylation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research.The Journal of organic chemistry · 2026Review
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Review
- Photocatalytic Cross-Coupling of Phenols and Heteroaryl Halides With Machine Learning-Guided Reaction Prediction.Angewandte Chemie (International ed. in English) · 2026Article
- Review
- Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques.JACS Au · 2025Article
- Using Yield Profiles to Study Reaction Mechanism.ACS catalysis · 2025Article
- Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case.Angewandte Chemie (International ed. in English) · 2024Article
- Article
- A dual-targeted drug inhibits cardiac ryanodine receptor CaLife science alliance · 2024Article
- Evaluation of functional group compatibility and development of reaction-accelerating additives in ammonium salt-accelerated hydrazinolysis of amides.Frontiers in chemistry · 2024Article
- Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.Nature communications · 2023Article
- Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis.Chemistry (Weinheim an der Bergstrasse, Germany) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.
Indexed as
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.