![[2602.13249] Boltz is a Strong Baseline for Atom-level Representation Learning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13249] Boltz is a Strong Baseline for Atom-level Representation Learning
Summary
The paper presents Boltz as a competitive baseline for atom-level representation learning in molecular tasks, particularly in ADMET property prediction and molecular generation.
Why It Matters
Understanding the capabilities of Boltz in atom-level representation learning is crucial for advancing molecular modeling and drug discovery. This research highlights the potential of protein-centric models in small-molecule applications, which could lead to more effective predictive models in biochemistry and pharmacology.
Key Takeaways
- Boltz demonstrates strong performance in atom-level representation for small molecules.
- It competes effectively with specialized models in ADMET property prediction tasks.
- The research suggests that protein-centric models can be valuable for small-molecule tasks.
- Boltz's capabilities in molecular generation and optimization are noteworthy.
- This study opens avenues for further exploration of protein models in diverse molecular applications.
Quantitative Biology > Biomolecules arXiv:2602.13249 (q-bio) [Submitted on 2 Feb 2026] Title:Boltz is a Strong Baseline for Atom-level Representation Learning Authors:Hyosoon Jang, Hyunjin Seo, Yunhui Jang, Seonghyun Park, Sungsoo Ahn View a PDF of the paper titled Boltz is a Strong Baseline for Atom-level Representation Learning, by Hyosoon Jang and 4 other authors View PDF HTML (experimental) Abstract:Foundation models in molecular learning have advanced along two parallel tracks: protein models, which typically utilize evolutionary information to learn amino acid-level representations for folding, and small-molecule models, which focus on learning atom-level representations for property prediction tasks such as ADMET. Notably, cutting-edge protein-centric models such as Boltz now operate at atom-level granularity for protein-ligand co-folding, yet their atom-level expressiveness for small-molecule tasks remains unexplored. A key open question is whether these protein co-folding models capture transferable chemical physics or rely on protein evolutionary signals, which would limit their utility for small-molecule tasks. In this work, we investigate the quality of Boltz atom-level representations across diverse small-molecule benchmarks. Our results show that Boltz is competitive with specialized baselines on ADMET property prediction tasks and effective for molecular generation and optimization. These findings suggest that the representational capacity of cutting-edge pr...
