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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint describes a novel machine learning method that learns reaction representations dynamically from text using fine-tuned language models combined with Gaussian process surrogates for multi-objective Bayesian optimisation. Prospective application to two complex reactions achieved high isolated yields (94% and 84%) and enantiomeric excess (99.6%) using under 3% of the design space, but the work is unpublished, uncontrolled, and limited to two test cases without independent validation.
Uncontrolled prospective method validation with retrospective simulation comparisons. Two distinct catalytic reaction systems: palladium-catalysed cyanation and three-objective asymmetric hydrogenation. Intervention: Dynamic language model-based representation learning for reaction conditions combined with Gaussian process surrogates in multi-objective Bayesian optimisation. Compared with: Descriptor libraries and one-hot encoding (in retrospective simulations only); no comparison in prospective experiments. Not stated.
Palladium-catalysed cyanation reached 94% isolated yield after two rounds of high-throughput experimentation (192 reactions total under 3% design space coverage) Three-objective asymmetric hydrogenation across chiral iridium and ruthenium catalysts delivered 84% isolated yield at 99.6% enantiomeric excess Language model approach achieved optimisation convergence in fewer experiments than descriptor libraries or one-hot encoding in retrospective nickel- and palladium-catalysed cross-coupling simulations
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This work is not clinical; it is relevant to synthetic chemists and computational chemists considering machine learning for reaction optimisation. The approach may reduce experimentation needed for complex multi-objective reaction design, but requires independent replication and peer review before adoption.
Early-stage proof-of-concept demonstrating a novel machine learning approach for reaction optimisation with proof-of-principle results, but limited to two prospective applications without peer review or independent replication.
As stated by the source record.
Quoted from the source exactly as published.
This work is not clinical; it is relevant to synthetic chemists and computational chemists considering machine learning for reaction optimisation. The approach may reduce experimentation needed for complex multi-objective reaction design, but requires independent replication and peer review before adoption.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Optimising chemical reactions across multiple objectives, such as yield, selectivity, and safety, is central to chemical synthesis, and model-driven approaches depend critically on how reaction components are represented. Established featurisations are either chemically uninformative, as with one-hot encodings, or, as with molecular descriptors, do not readily extend across chemically distinct components. For structurally and functionally diverse components, it is therefore unclear what a shared representation should contain. Constructing such a representation is itself a challenging research undertaking that must be revisited for each new reaction system. Here we bypass this step by learning the reaction representation dynamically from text. Textual descriptions of reaction conditions are encoded by a fine-tuned language model trained jointly with Gaussian process surrogates, yielding task-adaptive representations within a multi-objective Bayesian optimisation loop. Across nickel- and palladium-catalysed cross-couplings in both sequential and parallel experimentation regimes, this approach reaches optimisation convergence in fewer experiments than descriptor libraries or one-hot encoding. Applied prospectively to a palladium-catalysed cyanation spanning mixed ligand denticity and heterogeneous additives, and to a three-objective asymmetric hydrogenation across chiral iridium and ruthenium catalyst families, two rounds of high-throughput experimentation (192 reactions, under 3% of each design space) delivered conditions translating directly to gram scale in 94% and 84% isolated yield, the latter at 99.6% enantiomeric excess.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.