https://github.com/LigandPro/Bento
UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.
https://github.com/LigandPro/Bento
benchmark bioinformatics cheminformatics computational-chemistry drug-discovery hpc molecular-docking protein-ligand-docking python slurm structure-based-drug-design uv
Last synced: 23 days ago
JSON representation
UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.
- Host: GitHub
- URL: https://github.com/LigandPro/Bento
- Owner: LigandPro
- License: mit
- Created: 2025-12-29T12:39:50.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2026-03-04T07:57:37.000Z (5 months ago)
- Last Synced: 2026-03-04T13:50:34.711Z (5 months ago)
- Topics: benchmark, bioinformatics, cheminformatics, computational-chemistry, drug-discovery, hpc, molecular-docking, protein-ligand-docking, python, slurm, structure-based-drug-design, uv
- Language: Jupyter Notebook
- Size: 7.06 MB
- Stars: 3
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-computational-biology - Bento - ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks. (Benchmarks & Datasets / Clinical Trial)
- awesome-chemistry - Bento - ligand docking benchmark with annotation, pocket similarity, and HPC workflows. (Cheminformatics & Molecular Modeling)
README
# Bento
[](https://github.com/LigandPro/Bento/actions/workflows/ci.yml)
[](https://github.com/LigandPro/Bento/blob/main/LICENSE)
Bento is a benchmark repository for evaluating protein-ligand docking methods across curated datasets and prediction outputs.
## Highlights
- Unified benchmark assets for ML and classical docking methods.
- Legacy analysis pipeline preserved in `scripts/`.
- UV-first workflow for local development and CI.
- Reproducibility and HPC execution notes.
## Repository Layout
```text
bento/
├── annotated_ligands/ # Train/test tables with ligand-level annotations
├── annotations/ # Annotation maps and ligand class assets
├── datasets/ # Core benchmark datasets
├── predictions_full_raw/ # Raw predictions from docking methods
├── scripts/ # Legacy pipeline scripts (01..04)
├── similarity_scores/ # Similarity score artifacts
├── src/bento/ # Bento CLI wrapper and environment tooling
├── tests/ # Automated tests
├── docs/ # Reproducibility and HPC docs
└── slurm/ # Example SLURM job templates
```
## Quick Start (UV)
### 1. Sync environment
```bash
uv sync --no-editable
```
`--no-editable` is recommended to guarantee the `bento` console entrypoint is available in all Python 3.12 environments.
For development checks:
```bash
uv sync --no-editable --extra lint --extra test
```
For ligand annotation dependencies:
```bash
uv sync --no-editable --extra annotation
```
For pocket similarity dependencies (Linux/HPC):
```bash
uv sync --no-editable --extra similarity
```
### 2. Validate environment
```bash
uv run --extra annotation bento check-env --profile annotation
uv run bento check-env --profile similarity --glosa-dir /path/to/glosa_v2.2
```
### 3. Run pipeline commands
Ligand annotation:
```bash
uv run --extra annotation bento annotate-ligands \
--dataset-file datasets/tests.tsv \
--output-dir outputs/
```
Pocket similarity:
```bash
uv run bento compute-pocket-similarity \
--data-csv test_run/path_tests.tsv \
--protein-path path_protein \
--ligand-path path_ligand \
--bs-dir bs \
--glosa-dir /path/to/glosa_v2.2 \
--output-file similarity_scores/test_pocket_scores.tsv
```
Annotation mapping:
```bash
uv run bento map-annotations \
--tests-file datasets/tests.tsv \
--annotations-dir annotations \
--output-tests-file datasets/tests_annotated.tsv \
--output-tests-exploded-file datasets/tests_exploded_annotated.tsv
```
## Configuration
Legacy scripts use environment variables:
- `BENTO_WORKDIR`: repository root path (default: current repository root).
- `BENTO_DATABASES_DIR`: root path for external dataset files (default: `BENTO_WORKDIR`).
- `BENTO_GLOSA_DIR`: path to GLoSA directory (default: `/external/glosa`).
- `BENTO_REPO_ROOT`: optional override for CLI location of legacy scripts.
Example:
```bash
export BENTO_WORKDIR=/path/to/Bento
export BENTO_DATABASES_DIR=/path/to/datasets
export BENTO_GLOSA_DIR=/path/to/glosa_v2.2
```
## External Tool Requirements
Some steps require non-Python tools:
- PyMOL (for pocket extraction in script 02; installed with `--extra similarity`
on Linux/HPC).
- Java JDK and `g++` (for GLoSA tooling).
- GLoSA v2.2 executable and `AssignChemicalFeatures` class.
Build GLoSA once inside your `BENTO_GLOSA_DIR`:
```bash
g++ -c glosa.cpp
g++ -o glosa glosa.o
javac AssignChemicalFeatures.java
```
These tools are validated by:
```bash
uv run bento check-env --profile similarity --glosa-dir /path/to/glosa_v2.2
```
## Quality Checks
```bash
uv run ruff check .
uv run ruff format --check .
uv run pytest
```
## Reproducibility and HPC
- Reproducibility guide: [docs/reproducibility.md](docs/reproducibility.md)
- HPC setup: [docs/hpc.md](docs/hpc.md)
- SLURM templates: `slurm/`
## Citation
```bibtex
@software{bento_benchmark,
title={Bento: A Comprehensive Benchmark for Protein-Ligand Docking Methods},
author={LigandPro Team},
year={2024},
url={https://github.com/LigandPro/Bento}
}
```
## License
MIT. See [LICENSE](LICENSE).