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Add DockGen results, and update Zenodo links
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‎README.md‎

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# PoseBench
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[![Paper](http://img.shields.io/badge/arXiv-2405.14108-B31B1B.svg)](https://arxiv.org/abs/2405.14108)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.11199233.svg)](https://doi.org/10.5281/zenodo.11199233)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.11477766.svg)](https://doi.org/10.5281/zenodo.11477766)
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[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
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[![Docs](https://assets.readthedocs.org/static/projects/badges/passing-flat.svg)](https://bioinfomachinelearning.github.io/PoseBench/)
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<a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/config-hydra-89b8cd"></a>
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```bash
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# fetch, extract, and clean-up preprocessed Astex Diverse, PoseBusters Benchmark, DockGen, and CASP15 data (~3 GB) #
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cd data/
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wget https://zenodo.org/records/11199233/files/astex_diverse_set.tar.gz
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wget https://zenodo.org/records/11199233/files/posebusters_benchmark_set.tar.gz
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wget https://zenodo.org/records/11199233/files/dockgen_set.tar.gz
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wget https://zenodo.org/records/11199233/files/casp15_set.tar.gz
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wget https://zenodo.org/records/11477766/files/astex_diverse_set.tar.gz
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wget https://zenodo.org/records/11477766/files/posebusters_benchmark_set.tar.gz
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wget https://zenodo.org/records/11477766/files/dockgen_set.tar.gz
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wget https://zenodo.org/records/11477766/files/casp15_set.tar.gz
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tar -xzf astex_diverse_set.tar.gz
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tar -xzf posebusters_benchmark_set.tar.gz
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tar -xzf dockgen_set.tar.gz
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# fetch, extract, and clean-up benchmark method predictions to reproduce paper results (~19 GB) #
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# DiffDock predictions and results
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cd forks/DiffDock/
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wget https://zenodo.org/records/11199233/files/diffdock_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/diffdock_benchmark_method_predictions.tar.gz
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tar -xzf diffdock_benchmark_method_predictions.tar.gz
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rm diffdock_benchmark_method_predictions.tar.gz
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# FABind predictions and results
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cd forks/FABind/
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wget https://zenodo.org/records/11199233/files/fabind_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/fabind_benchmark_method_predictions.tar.gz
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tar -xzf fabind_benchmark_method_predictions.tar.gz
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rm fabind_benchmark_method_predictions.tar.gz
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# DynamicBind predictions and results
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cd forks/DynamicBind/
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wget https://zenodo.org/records/11199233/files/dynamicbind_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/dynamicbind_benchmark_method_predictions.tar.gz
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tar -xzf dynamicbind_benchmark_method_predictions.tar.gz
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rm dynamicbind_benchmark_method_predictions.tar.gz
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# NeuralPLexer predictions and results
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cd forks/NeuralPLexer/
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wget https://zenodo.org/records/11199233/files/neuralplexer_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/neuralplexer_benchmark_method_predictions.tar.gz
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tar -xzf neuralplexer_benchmark_method_predictions.tar.gz
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rm neuralplexer_benchmark_method_predictions.tar.gz
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# RoseTTAFold-All-Atom predictions and results
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cd forks/RoseTTAFold-All-Atom/
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wget https://zenodo.org/records/11199233/files/rfaa_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/rfaa_benchmark_method_predictions.tar.gz
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tar -xzf rfaa_benchmark_method_predictions.tar.gz
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rm rfaa_benchmark_method_predictions.tar.gz
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# TULIP predictions and results
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cd forks/TULIP/
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wget https://zenodo.org/records/11199233/files/tulip_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/tulip_benchmark_method_predictions.tar.gz
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tar -xzf tulip_benchmark_method_predictions.tar.gz
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rm tulip_benchmark_method_predictions.tar.gz
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# AutoDock Vina predictions and results
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cd forks/Vina/
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wget https://zenodo.org/records/11199233/files/vina_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/vina_benchmark_method_predictions.tar.gz
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tar -xzf vina_benchmark_method_predictions.tar.gz
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rm vina_benchmark_method_predictions.tar.gz
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# Astex Diverse, PoseBusters Benchmark, PoseBusters Benchmark (pocket-only), DockGen, and CASP15 consensus ensemble predictions and results
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# Astex Diverse, PoseBusters Benchmark (w/ pocket-only results), DockGen, and CASP15 consensus ensemble predictions and results
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cd data/test_cases/
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wget https://zenodo.org/records/11199233/files/astex_diverse_ensemble_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11199233/files/posebusters_benchmark_ensemble_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11199233/files/posebusters_benchmark_pocket_only_ensemble_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11199233/files/dockgen_ensemble_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11199233/files/casp15_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/astex_diverse_ensemble_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/posebusters_benchmark_ensemble_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/dockgen_ensemble_benchmark_method_predictions.tar.gz
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wget https://zenodo.org/records/11477766/files/casp15_ensemble_benchmark_method_predictions.tar.gz
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tar -xzf astex_diverse_ensemble_benchmark_method_predictions.tar.gz
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tar -xzf posebusters_benchmark_ensemble_benchmark_method_predictions.tar.gz
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tar -xzf posebusters_benchmark_pocket_only_ensemble_benchmark_method_predictions.tar.gz
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tar -xzf dockgen_ensemble_benchmark_method_predictions.tar.gz
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tar -xzf casp15_benchmark_method_predictions.tar.gz
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tar -xzf casp15_ensemble_benchmark_method_predictions.tar.gz
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rm astex_diverse_ensemble_benchmark_method_predictions.tar.gz
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rm posebusters_benchmark_ensemble_benchmark_method_predictions.tar.gz
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rm posebusters_benchmark_pocket_only_ensemble_benchmark_method_predictions.tar.gz
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rm dockgen_ensemble_benchmark_method_predictions.tar.gz
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rm casp15_benchmark_method_predictions.tar.gz
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rm casp15_ensemble_benchmark_method_predictions.tar.gz
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```
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**NOTE:** One can reproduce the *pocket-only* experiments with the PoseBusters Benchmark set by adding the argument `pocket_only_baseline=true` to each command below used to run PoseBusters Benchmark dataset inference with all the baseline methods, since the pocket-only versions of the dataset's holo-aligned predicted protein structures have also been included in the downloadable Zenodo archive `posebusters_benchmark_set.tar.gz` referenced above. However, be aware that one then needs to *rename* any existing directories containing PoseBusters Benchmark dataset inference results for each baseline method, to prevent these existing inference directories from being merged with new pocket-only results. Please see the config files within `configs/data/`, `configs/model/`, and `configs/analysis/` for more details.
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python3 src/data/components/esmfold_apo_to_holo_alignment.py dataset=astex_diverse num_workers=1
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```
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**NOTE:** The preprocessed DockGen and CASP15 data available via [Zenodo](https://doi.org/10.5281/zenodo.11199233) provide pre-holo-aligned predicted protein structures for these respective datasets.
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**NOTE:** The preprocessed DockGen and CASP15 data available via [Zenodo](https://doi.org/10.5281/zenodo.11477766) provide pre-holo-aligned predicted protein structures for these respective datasets.
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</details>
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### How to run inference with `TULIP`
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Gather all template ligands pre-generated by `TULIP` and collate the resulting ligand fragment SDF files
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Gather all template ligands generated by `TULIP` via its dedicated [GitHub repository](https://github.com/BioinfoMachineLearning/tulip) and collate the resulting ligand fragment SDF files
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```bash
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python3 src/data/tulip_output_extraction.py dataset=posebusters_benchmark

‎docs/source/acknowledgements.rst‎

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‎docs/source/available_methods.rst‎

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.. note::
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Have a new method to add? Please let us know by creating a pull request. We would be happy to work with you to integrate new methodology into this benchmark!

‎docs/source/bonus.rst‎

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.. mdinclude:: ../../README.md
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.. image:: ./_static/WorkBench.jpeg
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:alt: My brain after building PoseBench

‎docs/source/citing_this_work.rst‎

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‎docs/source/comparative_plots.rst‎

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‎docs/source/data_preparation.rst‎

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.. mdinclude:: ../../README.md
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‎docs/source/ensemble_inference.rst‎

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.. note::
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In addition to having `consensus` as an available value for `ensemble_ranking_method`, one can also set `ensemble_ranking_method=ff` to have the method ensemble's top-ranked predictions selected using the criterion of "minimum (molecular dynamics) force field energy" (albeit while incurring a very large runtime complexity).

‎docs/source/for_developers.rst‎

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‎docs/source/method_inference.rst‎

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