{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen R"],"funding":["National Natural Science Foundation of China","National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["255"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12375046"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(1)"],"pubmed_abstract":["Fast and efficient automated exploration of reaction pathways is essential for studying reaction mechanisms and advancing data-driven approaches for reaction development and catalyst design. Here, we present a new program (utilizing Python and Fortran), capable of conducting automated, fast, and efficient exploration of reaction pathways for potential energy surfaces (PES) studies. This program integrates quantum mechanics and rule-based methodologies, underpinned by a Large Language Model-assisted chemical logic. Both active-learning methods in transition states sampling and parallel multi-step reaction searches with efficient filtering help enhance efficiency and accelerate PES searching. Its effectiveness and versatility in automating searches are exemplified through case studies of multi-step reactions, including the organic cycloaddition reaction, asymmetric Mannich-type reaction, and organometallic Pt-catalyzed reaction. ARplorer's capability to scale up for high-throughput screening significantly enhances its utility, positioning it as an efficient tool for data-driven reaction development and catalyst design."],"journal":["Communications chemistry"],"pubmed_title":["Large language model guided automated reaction pathway exploration."],"pmcid":["PMC12375046"],"funding_grant_id":["Nos. 22373118 and 22231002"],"pubmed_authors":["Liu Y","Li Y","Chen Z","Ke Z","Lin J","Chen R","Yang F"],"additional_accession":[]},"is_claimable":false,"name":"Large language model guided automated reaction pathway exploration.","description":"Fast and efficient automated exploration of reaction pathways is essential for studying reaction mechanisms and advancing data-driven approaches for reaction development and catalyst design. Here, we present a new program (utilizing Python and Fortran), capable of conducting automated, fast, and efficient exploration of reaction pathways for potential energy surfaces (PES) studies. This program integrates quantum mechanics and rule-based methodologies, underpinned by a Large Language Model-assisted chemical logic. Both active-learning methods in transition states sampling and parallel multi-step reaction searches with efficient filtering help enhance efficiency and accelerate PES searching. Its effectiveness and versatility in automating searches are exemplified through case studies of multi-step reactions, including the organic cycloaddition reaction, asymmetric Mannich-type reaction, and organometallic Pt-catalyzed reaction. ARplorer's capability to scale up for high-throughput screening significantly enhances its utility, positioning it as an efficient tool for data-driven reaction development and catalyst design.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-05-09T17:58:22.959Z","creation":"2026-04-08T01:08:16.498Z"},"accession":"S-EPMC12375046","cross_references":{"pubmed":["40849562"],"doi":["10.1038/s42004-025-01630-y"]}}