ArticleNature communications2025
Highly parallel optimisation of chemical reactions through automation and machine intelligence.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- A Practical Guide to Bayesian Optimization for Organic Chemists.Chem catalysis · 2026Article
- Bayesian optimization- and high-throughput-guided synthesis of monofluoroalkenesChemical science · 2026Article
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- Accelerate Your Science: Direct-to-Biology Strategies in Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- Yield Smarter, Not Harder: Good Practices for Machine Learning of Reaction Outcomes.Journal of the American Chemical Society · 2026Article
- Machine Learning-Guided Tailored Synthesis of Single Room-Temperature Phosphorescent Carbon Dots.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Decoding α-MoCSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
- A chemically-aware validation framework for benchmarking large language models in materials synthesis planning.Journal of cheminformatics · 2026Article
- Frugal Sampling Strategies for Navigating Complex Reaction Spaces.Organic process research & development · 2026Article
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments.ACS nanoscience Au · 2026Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Highly parallel optimisation of chemical reactions through automation and machine intelligence.Nature communications · 2025Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- "Naked Nickel"-Catalyzed Heteroaryl-Heteroaryl Suzuki-Miyaura Coupling.Angewandte Chemie (International ed. in English) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We report the development and application of a scalable machine learning (ML) framework (Minerva) for highly parallel multi-objective reaction optimisation with automated high-throughput experimentation (HTE). Minerva demonstrates robust performance with experimental data-derived benchmarks, efficiently handling large parallel batches, high-dimensional search spaces, reaction noise, and batch constraints present in real-world laboratories. Validating our approach experimentally, we apply Minerva in a 96-well HTE reaction optimisation campaign for a nickel-catalysed Suzuki reaction, tackling challenges in non-precious metal catalysis. Our approach effectively navigates the complex reaction landscape with unexpected chemical reactivity, outperforming traditional experimentalist-driven methods. Extending to industrial applications, we deploy Minerva in pharmaceutical process development, successfully optimising two active pharmaceutical ingredient (API) syntheses. For both a Ni-catalysed Suzuki coupling and a Pd-catalysed Buchwald-Hartwig reaction, our approach identifies multiple conditions achieving >95 area percent (AP) yield and selectivity, directly translating to improved process conditions at scale.
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.