1. Rachel: A general-purpose language model directs and revises retrosynthetic routes1
The unreviewed Rachel study lets a language model revise multistep synthesis plans in a stateful checking environment. It closed routes for 111 of 120 benchmark targets and 24 of 25 difficult targets, with terminal precursors independently source-resolved after planning. This tests sustained route decisions, not laboratory synthesis. Our account is limited to the original abstract.1
2. Learning continuous reaction paths for transition-state prediction2
The unreviewed MARC-TS framework learns continuous reaction paths before locating transition states. Quantum-chemical optimisation and vibrational analysis confirmed first-order saddle-point candidates for 405 of 410 predictions. In a separate comparison, learned paths improved optimisation convergence over geometric interpolation. The work connects path modelling to quantum refinement; reporting is limited to the original abstract.2
3. Using Data Science Tools to Explore Rate Matching in a Nickel-Catalyzed Cross-Electrophile Coupling of Alkyl and Aryl Halides (Cl, Br) with a Tridentate Monoanionic Ligand3
Researchers combine electroanalytical measurements with machine learning to relate activation rates to nickel-catalysed cross-electrophile coupling yields. A new ligand enables comparison across alkyl and aryl chlorides and bromides; choosing matched halides changes synthetic performance predictably. The study connects rate measurements to reaction design. This account is restricted to the publisher-deposited abstract.3
4. Deep Learning-Guided Interface Engineering Stabilizes Oligomeric Enzymes4
DeepIE uses deep learning to redesign flexible subunit interfaces in an oligomeric enzyme. The leading formate-dehydrogenase variant retained wild-type catalytic activity while extending its half-life at 50 °C by roughly 500-fold. Simulations connect the result to reduced flexibility and stronger hydrophobic packing. This measured enzyme-stability advance is reported from the publisher-deposited abstract only.4
5. Differentiable mass transfer modeling enables wet lab validated inverse analysis of liquid chromatography5
An unreviewed chromatography study combines mechanistic modelling with differentiable learning to estimate transport and adsorption parameters. Using three gradient programmes improved parameter recovery in simulations; laboratory comparisons placed 89.3% of retention-time predictions within ten seconds. The work targets more informative analytical method development for autonomous laboratories. Our account is restricted to the deposited abstract.5
References
Li, Qisheng; Jiang, Shunchao; Qi, Chen; Su, Xin; Han, Da; Chen, Guangyong. Rachel: A general-purpose language model directs and revises retrosynthetic routes. arXiv; 2026; Preprint v1; not peer reviewed. DOI: 10.48550/arXiv.2609.25118. Accessed 2026-09-23T16:20:21.031Z.
Source evidence and access
Abstract and submission history, arXiv:2609.25118; Cite-as DOI field (arXiv marks DataCite registration pending)
Evidence paraphrase: The unreviewed Rachel study lets a language model revise multistep synthesis plans in a stateful checking environment. It closed routes for 111 of 120 benchmark targets and 24 of 25 difficult targets, with terminal precursors independently source-resolved after planning. This tests sustained route decisions, not laboratory synthesis. Our account is limited to the original abstract.
Original arXiv abstract, submission history, comments and repository DOI inspected; arXiv labels DOI registration pending. Full text not reviewed.
Yang, Yexiang; Zhong, Linlin. Learning continuous reaction paths for transition-state prediction. arXiv; 2026; Preprint v1; not peer reviewed. DOI: 10.48550/arXiv.2609.25523. Accessed 2026-09-23T16:20:21.032Z.
Source evidence and access
Abstract and submission history, arXiv:2609.25523; Cite-as DOI field (arXiv marks DataCite registration pending)
Evidence paraphrase: The unreviewed MARC-TS framework learns continuous reaction paths before locating transition states. Quantum-chemical optimisation and vibrational analysis confirmed first-order saddle-point candidates for 405 of 410 predictions. In a separate comparison, learned paths improved optimisation convergence over geometric interpolation. The work connects path modelling to quantum refinement; reporting is limited to the original abstract.
Original arXiv abstract, submission history, comments and repository DOI inspected; arXiv labels DOI registration pending. Full text not reviewed.
Haruka Takenaka; Alexandra J. Ring; Avijit Hazra; Therese H. Wild; Samuel M. R. Powell; Tianhua Tang; Sarah E. Reisman; Matthew S. Sigman. Using Data Science Tools to Explore Rate Matching in a Nickel-Catalyzed Cross-Electrophile Coupling of Alkyl and Aryl Halides (Cl, Br) with a Tridentate Monoanionic Ligand. Journal of the American Chemical Society; 2026; Peer-reviewed journal article; first online publication. DOI: 10.1021/jacs.6c10810. Accessed 2026-09-23T16:05:22.557Z.
Source evidence and access
Publisher-deposited abstract and first-online metadata, https://api.crossref.org/works/10.1021/jacs.6c10810
Evidence paraphrase: Researchers combine electroanalytical measurements with machine learning to relate activation rates to nickel-catalysed cross-electrophile coupling yields. A new ligand enables comparison across alkyl and aryl chlorides and bromides; choosing matched halides changes synthetic performance predictably. The study connects rate measurements to reaction design. This account is restricted to the publisher-deposited abstract.
Restricted to original publisher/repository-deposited abstract and metadata retrieved through Crossref; full text not inspected.
Wei-Jie Zhan; Yang Zhuo; Zhi-Hao He; Xin-Yi Lu; Zhi-Jun Zhang; Xiao-Yu You; Qing-Chao Jiang; Kun Shi; Hui-Lei Yu. Deep Learning-Guided Interface Engineering Stabilizes Oligomeric Enzymes. ACS Catalysis; 2026; Peer-reviewed journal article; first online publication. DOI: 10.1021/acscatal.6c05133. Accessed 2026-09-23T16:05:24.859Z.
Source evidence and access
Publisher-deposited abstract and first-online metadata, https://api.crossref.org/works/10.1021/acscatal.6c05133
Evidence paraphrase: DeepIE uses deep learning to redesign flexible subunit interfaces in an oligomeric enzyme. The leading formate-dehydrogenase variant retained wild-type catalytic activity while extending its half-life at 50 °C by roughly 500-fold. Simulations connect the result to reduced flexibility and stronger hydrophobic packing. This measured enzyme-stability advance is reported from the publisher-deposited abstract only.
Restricted to original publisher/repository-deposited abstract and metadata retrieved through Crossref; full text not inspected.
Baochen Li; Zihan Zhou; Yanqing Yu; Sen Lin; Peng Wang; Tianshu Yu; Xiaoxue Wang. Differentiable mass transfer modeling enables wet lab validated inverse analysis of liquid chromatography. ChemRxiv; 2026; Preprint v1; not peer reviewed. DOI: 10.26434/chemrxiv.15009352/v1. Accessed 2026-09-23T16:05:19.312Z.
Source evidence and access
Publisher-deposited abstract and first-online metadata, https://api.crossref.org/works/10.26434/chemrxiv.15009352/v1
Evidence paraphrase: An unreviewed chromatography study combines mechanistic modelling with differentiable learning to estimate transport and adsorption parameters. Using three gradient programmes improved parameter recovery in simulations; laboratory comparisons placed 89.3% of retention-time predictions within ten seconds. The work targets more informative analytical method development for autonomous laboratories. Our account is restricted to the deposited abstract.
Restricted to original publisher/repository-deposited abstract and metadata retrieved through Crossref; full text not inspected.
Publication record
Published 2026-09-23.
Sources, selection and claims were checked in an independent AI editorial review, followed by the AI editor's approval. This is not academic peer review.
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