Chemical Language Models

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11 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

General-purpose foundation models surpass specialized ones on chemical property prediction tasks
Oct 30, 2026Stepan N. Ostarkov, Gregorii A. Semakin, Ivan A. Bespalov +1Molecular Property Prediction Chemical Language Models
N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, 119334, Moscow, Russia
Same molecule, Different Knowledge: Cross-Identifier Binding Failures in LLMs For chemistry
Oct 22, 2026Khouloud Saadi, Di WangChemical Language Models Chemical Reasoning in LLMs
Department of Computer Science, King Abdullah University of Science and Technology, KAUST, Thuwal, Saudi Arabia
Does Pretraining Teach Chemical Language Models Chemistry?
Oct 13, 2026Leif Sieben, Yoel ZimmermannChemical Language Models Language Model Pretraining
Harvard University, Cambridge, MA, USA
Probing Chemical Language Models: Effects of Pre-training and Fine-tuning
Jul 2, 2026Anna Karnysheva, Dietrich Klakow, Ji-Ung LeeLanguage Model Pretraining Chemical Language Models
1RTG Neuroexplicit Models · 2Spoken Language Systems · Saarland University +1
Uncertainty-aware reinforcement learning for chemical language models
Jun 23, 2026Borja Medina, Jon Paul JanetChemical Language Models Molecular Generation
Molecular AI, Discovery Sciences, BioPharmaceuticals R&D AstraZeneca AB Gothenburg, Sweden
What Does a Chemical Language Model Know About Molecules?
Jun 22, 2026Christian Kenneth, Etowah Adams, Liam Bai +1Chemical Language Models Mechanistic Interpretability
Independent · Department of Systems Biology, Columbia University, New York · Generate:Biomedicines, Massachusetts +1
From Syntax to Semantics: Unveiling the Emergence of Chirality in SMILES Translation Models
May 11, 2026Zehao Li, Yasuhiro Yoshikai, Shumpei Nemoto +2Chemical Language Models Transformer Interpretability
Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan · The Institute of Statistical Mathematics (ISM), Research Organization of Information and Systems, 190-8562 Tachikawa, Tokyo, Japan
A chemical language model for reticular materials design
Mar 20, 2026Dhruv Menon, Vivek Singh, Xu Chen +7Chemical Language Models Materials Science
Department of Chemical Engineering & Biotechnology, University of Cambridge, Cambridge, UK · Department of Chemistry, University of California – Berkeley, Berkeley, CA, USA · Bakar Institute of Digital Materials for the Planet, Berkeley, CA, USA +2
BERTology of Molecular Property Prediction
Mar 13, 2026Mohammad Mostafanejad, Paul Saxe, T. Daniel CrawfordChemical Language Models Molecular Property Prediction
Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, USA · Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
ChemBART: Pre-training a Reaction Language Model for Comprehensive Synthesis Planning
Jan 6, 2026Kenan Li, Yijian Zhang, Jin Wang +4Chemical Language Models Chemical Reaction Prediction
State Key Laboratory of Analytical Chemistry for Life Science, Kuang Yaming Honors School, Chemistry and Biomedicine Innovation Centre (ChemBIC), ChemBioMed Interdisciplinary Research Centre at Nanjing University, Engineering Research Centre of Protein and Peptide Medicine of the Ministry of Education, Institute for Brain Sciences, Nanjing University, Nanjing 210023, China. · Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
A Large Scale Investigation of Scaling Limits in Chemical Language Models
Aug 19, 2025Roshan Balaji, Kamran Chitsaz, Quentin Fournier +2Chemical Language Models Language Model Scaling Laws
BioSystems Engineering and Control Lab · Wadhwani School of Data Science and AI, IIT Madras · The Centre for Integrative Biology and Systems medicinE (IBSE) +5

Oct 30, 2026J/K move · Enter open · S save

General-purpose foundation models surpass specialized ones on chemical property prediction tasks

Stepan N. Ostarkov, Gregorii A. Semakin, Ivan A. Bespalov, Michael G. Medvedev

N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, 119334, Moscow, Russia

Molecular Property Prediction Chemical Language Models Molecular Foundation Models

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Foundation models (FMs) learn reusable representations from large collections of unlabeled data. In chemistry the corresponding data mainly consists of molecular structures, which are abundant, whereas experimental measurements are scarce. Chemical FMs such as ChemBERTa and MoLFormer were therefore trained on diverse molecular structures to enable fine-tuning on small experimental datasets (less than 100 datapoints in most realistic scenarios) and subsequent property prediction. At the same time, general-purpose FMs such as GPT-6 Astra and Claude Opus 5.5 were trained on virtually all publicly available data, including knowledge of the humankind on molecular properties and laws governing them. Here we compare accuracies of chemical and general-purpose FMs on tasks from various areas of chemistry. Strikingly, general-purpose GPT-6 Astra outperforms the currently eligible chemical FMs on five physicochemical and quantum-chemical regression tasks when only 60 experimental datapoints are available. Only when provided 10x datapoints do the best chemical FMs reach performance of GPT-6 Astra: on 1,000 and 10,000 points they show performance on par with Astra on 60 and 1,000 points, respectively. Thus, counterintuitively, frontier general-purpose FMs are better suited for guiding chemical research in data-scarce areas than specialized chemical FMs, suggesting that learning relevant primitives (e.g. chemical structures) does not substitute training on relevant humankind knowledge.