| --- |
| license: mit |
| tags: |
| - molecule-generation |
| - flow-matching |
| - meta-flow-maps |
| - inference-time-guidance |
| - dna |
| --- |
| |
| # MultiMFM |
|
|
| Checkpoints and data for **Multimodal Meta Flow Maps**: inference-time steering of pretrained |
| generators, for molecules and DNA. |
|
|
| [Code](https://github.com/shiyiwang958/MultiMFM) · |
| [Project page](https://shiyiwang958.github.io/multimodal-meta-flow-maps/) |
|
|
| ## Download |
|
|
| The files are laid out like the code repository, so fetch them from inside a checkout: |
|
|
| ```bash |
| git clone https://github.com/shiyiwang958/MultiMFM && cd MultiMFM |
| python scripts/download_artifacts.py # everything, ~1.5 GB, checksum-verified |
| python scripts/download_artifacts.py --only qm9 # or just one of qm9, geom, dna |
| ``` |
|
|
| ## Contents |
|
|
| ``` |
| checkpoints/ |
| qm9/ base_model, mfm_student_ce_esd, property_regressors 47 MB |
| geom/ base_model, mfm_student, fingerprint_head 422 MB |
| dna/ base_model, dmfm_student*, c0_regressors 499 MB |
| data/ |
| dna/ loop-seq cyclizability windows 385 MB |
| geom/ GEOM-Drugs fingerprints (steering targets) 173 MB |
| ``` |
|
|
| Each folder also holds the arguments its model was trained with. What each model does is in |
| the code repository's per-experiment guides (`docs/`). |
|
|
| ## License |
|
|
| MIT. Uses TFG-Flow (Lin et al., ICLR 2025), GEOM-Drugs (Axelrod & Gómez-Bombarelli, 2022; |
| CC0) and loop-seq data from Basu et al. (Nature, 2021); see the code repository's |
| `THIRD_PARTY.md`. |
|
|
