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MoLFormer is a chemical foundation model trained on 1.1 billion molecules with linear attention transformers, achieving state-of-the-art accuracy in quantum chemical properties and ADMET prediction.
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MoLFormer is a chemical foundation model trained on 1.1 billion molecules with linear attention transformers, achieving state-of-the-art accuracy in quantum chemical properties and ADMET prediction.
Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:09:40 UTC
Use this citation format when referencing MoLFormer in scientific publications and benchmark papers.
@software{molformer_2026,
title = {{MoLFormer}},
author = {{IBM Research}},
year = {2026},
url = {https://github.com/IBM/molformer},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}Live scientific citations queried from Europe PMC and PubMed for MoLFormer.
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MoLFormer Scientific Architecture · Credit: IBM Research · Open original ↗
Research and editorial use Source & attribution ↗ License ↗
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