Large-scale chemical language representations capture molecular structure and properties
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MoLFormer is a high-capacity molecular SMILES transformer model incorporating relative position embedding.
“MoLFormer (Ross et al. 2022), a high-capacity molecular SMILES transformer model incorporating relative position embedding.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Ross et al. 2022
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- Authors
- Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., & Das, P.
- Affiliations
- IBM (United States) (US)
- Corresponding author
- Ross, Jerret
- Das, Payel
- Venue
- Nature Machine Intelligence
- Published
- 2022-12-21
- Volume
- 4
- Issue
- 12
- Pages
- 1256-1264
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4313485929
- Cited by
- 590
- References
- 46
- Citations by year
- 2026: 222 · 2025: 212 · 2024: 112 · 2023: 42 · 2022: 2
- FWCI
- 49.91
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- molecular embeddings, Transformer encoder, rotary positional embeddings, linear attention, SMILES, molecular property prediction, quantum chemical properties, attention analysis, drug discovery, material design, self-supervised learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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