| --- |
| pretty_name: Physics-Inspired Landscape Learning and Flow Matching |
| task_categories: |
| - other |
| tags: |
| - molecular-generation |
| - structure-based-drug-design |
| - trajectories |
| --- |
| |
| # Physics-Inspired Landscape Learning and Flow Matching |
|
|
| ## LandFlow trajectory supervision |
|
|
| This repository contains the 3,000-trajectory bank used to train the LandFlow |
| route potential, the fixed CrossDocked split, atom-number metadata, reference |
| metric cache, and the PAFlow-ready CrossDocked pocket data required by the |
| reproduction workflow. |
|
|
| The bank contains 50 recorded states per trajectory (150,000 supervised state |
| examples). It was built from 100 training pockets using three frozen PAFlow |
| sampling settings and does not contain the paper test pockets as training |
| trajectories. |
|
|
| The CrossDocked portion contains the processed LMDB used directly by PAFlow, |
| the 93-pocket test receptor set, and the compressed pocket10 source files used |
| to regenerate processed data. The source archive expands to about 15 GB and is |
| kept compressed because it contains roughly 389,000 small files. |
|
|
| CrossDocked2020 is redistributed under the CC0 1.0 Universal Public Domain |
| Dedication. See `data/CrossDocked2020_LICENSE.txt` and the original distribution |
| at https://bits.csb.pitt.edu/files/crossdock2020/. |
|
|
| Use the matching LandFlow GitHub release to download and extract the data: |
|
|
| ```bash |
| python scripts/download_artifacts.py --include-crossdocked --extract-test-set |
| python scripts/verify_install.py --require-crossdocked |
| ``` |
|
|
