plinder 0.1.17

PLINDER: The Protein-Ligand INteraction Dataset and Evaluation Resource

The Protein Ligand INteractions Dataset and Evaluation Resource

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📚 About

PLINDER, short for protein ligand interactions dataset and evaluation resource, is a comprehensive, annotated, high quality dataset and resource for training and evaluation of protein-ligand docking algorithms:

• > 400k PLI systems across > 11k SCOP domains and > 50k unique small molecules
• 500+ annotations for each system, including protein and ligand properties, quality, matched molecular series and more
• Automated curation pipeline to keep up with the PDB
• 14 PLI metrics and over 20 billion similarity scores
• Unbound (apo) and predicted Alphafold2 structures linked to holo systems
• train-val-test splits and ability to tune splitting based on the learning task
• Robust evaluation harness to simplify and standard performance comparison between models.

The PLINDER project is a community effort, launched by the University of Basel, SIB Swiss Institute of Bioinformatics, VantAI, NVIDIA, MIT CSAIL, and will be regularly updated.

To accelerate community adoption, PLINDER will be used as the field’s new Protein-Ligand interaction dataset standard as part of an exciting competition at the upcoming 2024 Machine Learning in Structural Biology (MLSB) Workshop at NeurIPS, one of the field's premiere academic gatherings. More details about the competition will be announced shortly.

🏅 Gold standard benchmark sets

As part of PLINDER resource we provide train, validation and test splits that are curated to minimize the information leakage based on protein-ligand interaction similarity. In addition, we have prioritized the systems that has a linked experimental apo structure or matched molecular series to support realistic inference scenarios for hit discovery and optimization. Finally, a particular care is taken for test set that is further prioritized to contain high quality structures to provide unambiguous ground-truths for performance benchmarking.

Moreover, as we enticipate this resource to be used for benchmarking a wide range of methods, including those simultaneously predicting protein structure (aka. co-folding) or those generating novel ligand structures, we further stratified test (by novel ligand, pocket, protein or all) to cover a wide range of tasks.

👨💻 Getting Started

The PLINDER dataset is provided in two ways:

• You can either use the files from the dataset directly using your preferred tooling by downloading the data from the public bucket,
• or you can utilize the dedicated plinder Python package for interfacing the data.

Downloading the dataset

The dataset can be downloaded from the bucket with gsutil.

$ export PLINDER_RELEASE=2024-06 # Current release
$ export PLINDER_ITERATION=v2 # Current iteration
$ mkdir -p ~/.local/share/plinder/${PLINDER_RELEASE}/${PLINDER_ITERATION}/
$ gsutil -m cp -r "gs://plinder/${PLINDER_RELEASE}/${PLINDER_ITERATION}/*" ~/.local/share/plinder/${PLINDER_RELEASE}/${PLINDER_ITERATION}/

For details on the sub-directories, see Documentation.

Installing the Python package

plinder is available on PyPI.

pip install plinder

📝 Documentation

A more detailed description is available on the documentation website.

📃 Citation

Durairaj, Janani, Yusuf Adeshina, Zhonglin Cao, Xuejin Zhang, Vladas Oleinikovas, Thomas Duignan, Zachary McClure, Xavier Robin, Gabriel Studer, Daniel Kovtun, Emanuele Rossi, Guoqing Zhou, Srimukh Prasad Veccham, Clemens Isert, Yuxing Peng, Prabindh Sundareson, Mehmet Akdel, Gabriele Corso, Hannes Stärk, Gerardo Tauriello, Zachary Wayne Carpenter, Michael M. Bronstein, Emine Kucukbenli, Torsten Schwede, Luca Naef. 2024. “PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource.” bioRxiv ICML'24 ML4LMS

Please see the citation file for details.