LMI4Boltz
Boltz-2-compatible low-memory fork with extra chunking and bfloat16 controls.
Overview
LMI4Boltz is a low-memory Boltz-2 fork designed to preserve Boltz-2 complex-prediction and affinity workflows while exposing additional chunking and mixed-precision controls for constrained GPU environments. It supports the same broad biomolecular input space as Boltz-2, including proteins, nucleic acids, ligands, cyclic biopolymers, and restraint-guided co-folding, while adding explicit knobs for transition, triangle-attention, outer-product, and threshold chunk sizing together with optional bfloat16 inference.
Run LMI4Boltz on Neurosnap
The LMI4Boltz online webserver allows anybody with a Neurosnap account to run and access LMI4Boltz, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Boltz-2-compatible biomolecular complex prediction and affinity-estimation workflow.
- Additional chunk-size controls for transition, triangle-attention, and outer-product modules.
- Low-memory tuning for large systems and constrained GPU environments.
- Optional bfloat16 inference mode for reduced memory pressure.
- Support for cyclic biopolymers, ligands, covalent restraints, pocket restraints, and contact restraints.
- Retains Boltz-style inference-time potentials and affinity molecular-weight correction controls.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access LMI4Boltz using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using LMI4Boltz in publications or research outputs.
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Litfin, T. LMI4Boltz. GitHub repository. https://github.com/tlitfin/lmi4boltz |
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Passaro, S., Corso, G., Wohlwend, J., Reveiz, M., Thaler, S., Somnath, V. R., Getz, N., Portnoi, T., Roy, J., Stark, H., Kwabi-Addo, D., Beaini, D., Jaakkola, T., & Barzilay, R. (2025). Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. |
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Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: making protein folding accessible to all. Nature Methods. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |