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ChemBART: Pre-training a Reaction Language Model for Comprehensive Synthesis Planning — arXiv:2601.02915

Source: arXiv (en)
Original: https://arxiv.org/abs/2601.02915
License (as archived): CC0-1.0
Archived: 2026-10-25T08:36:38.221Z
SHA-256 of the archived text: 320478fed352a26a9674944ff68f48aebc9fc8001b5bbeb77faa7ef9ed178490
Stable URL of this page: https://metraipolis.com/documento/dc_ee3ebb45d7fc

How to cite

ChemBART: Pre-training a Reaction Language Model for Comprehensive Synthesis Planning — arXiv:2601.02915 — arXiv (en), https://arxiv.org/abs/2601.02915 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ee3ebb45d7fc (Metraipolis archive dc_ee3ebb45d7fc, sha256 320478fed352a26a9674944ff68f48aebc9fc8001b5bbeb77faa7ef9ed178490)

BibTeX

@misc{dcee3ebb45d7fc,
  title = {ChemBART: Pre-training a Reaction Language Model for Comprehensive Synthesis Planning — arXiv:2601.02915},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2601.02915},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ee3ebb45d7fc; sha256 320478fed352a26a9674944ff68f48aebc9fc8001b5bbeb77faa7ef9ed178490},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2601.02915. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent advances in large language models (LLMs) have demonstrated transformative potential across diverse fields. However, in computer-aided synthesis planning (CASP), current LLM methodologies are often restricted to single-molecule representations, limiting their application to isolated, single-task scenarios such as precursor prediction. Addressing this gap, we present ChemBART, a BART-based language model that adopts a reaction-centric pre-training strategy. By leveraging a mask-filling pre-training task on holistic reaction expressions (Reactant > Reagent > Product), ChemBART effectively solves a variety of chemical problems, including precursor/reagent generation, temperature-yield regression, and molecular property classification. Building on these capabilities, we demonstrate the integration of ChemBART with Monte Carlo tree search (MCTS) for multi-step synthesis route design, where the model serves to optimize the policy and value functions within a reinforcement learning framework. Beyond single-molecule pre-trained LLMs that are constrained to specific applications, ChemBART unifies diverse chemical tasks to facilitate synthesis planning with comprehensive implementation information. Most significantly, the proposed multi-step synthesis routes and reaction conditions were validated in the wet lab, confirming the discovery of shorter synthetic pathways with a >20% yield improvement over established literature benchmarks. Our work highlights the potential of reaction-focused pre-training and showcases the broad utility of ChemBART in closing the loop between in silico prediction and wet-lab synthesis.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2601.02915 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-25T08:31:27.814Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).