{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Pinder provides access to a large protein-protein interaction dataset and tools for training and evaluating protein docking algorithms, including paired predicted and apo structures for flexible docking.","skillfed_tags":["protein-docking","structural-biology","dataset"],"use_cases":["Train protein-protein docking models using the large paired dataset of holo and apo structures","Benchmark docking algorithms against gold-standard test sets included in the dataset","Access preprocessed protein structure data and metadata for structural biology research","Develop flexible docking methods using predicted and experimental structure pairs","Evaluate protein interaction prediction models on standardized benchmarks"],"what_it_does":"Pinder is a dataset and resource for protein-protein docking research, providing access to a large collection of protein structures and interaction data hosted on Google Cloud Storage. It includes monomer structures, ground-truth dimer complexes, predicted structures, and apo conformations\u2014the first dataset to pair predicted and apo structures for training flexible docking methods. The dataset is approximately 500 times larger than previous state-of-the-art datasets.\n\nThe Python API handles automatic downloading and caching of dataset files to a local directory (defaulting to ~/.local/share/pinder), with command-line tools for managing downloads and updates. It depends on 19 runtime packages including torch, torch-geometric, biotite, and pandas, making it suitable for machine learning workflows. The dataset requires approximately 700 GB of disk space when fully unpacked.","worth_installing":"Yes, if you are actively developing or benchmarking protein docking algorithms and have approximately 700 GB of disk space available. The dataset is substantially larger than prior resources and uniquely includes paired predicted and apo structures. However, maintenance is dormant, so verify compatibility with your current PyTorch and torch-geometric versions before committing to a production workflow. For exploratory work or small-scale evaluation, the download overhead may not justify the install."},"id":"pinder","links":{"html":"https://skillfed.io/packages/pinder","md":"https://skillfed.io/packages/pinder.md","pypi":"https://pypi.org/project/pinder/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-11-15","license_spdx":null,"license_treatment":"permissive","name":"pinder","python_support":"supports_current","summary":"PINDER: The Protein INteraction Dataset and Evaluation Resource"},"popularity":{"monthly_downloads":105620,"position":12688,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}