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scikit-fingerprints/ASAP_OpenADMET_pIC50_SARS-CoV-2

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Hugging Face2026-04-04 更新2026-04-12 收录
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--- license: cc0-1.0 task_categories: - tabular-regression - graph-ml - text-classification tags: - chemistry - biology - medical pretty_name: ASAP-OpenADMET pIC50 SARS-CoV-2 size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: "pic50_sars_cov_2.csv" --- # ASAP-OpenADMET pIC50 SARS-CoV-2 ASAP_OpenADMET_pIC50_SARS-CoV-2 dataset from the ASAP Discovery-OpenADMET Antiviral Drug Discovery Challenge [[1]](#1) [[2]](#2) [[3]](#3). It is intended to be used through [scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library. The task is to predict pIC50 of molecules against SARS-CoV-2. | **Characteristic** | **Description** | |:------------------:|:-----------------:| | Tasks | 1 | | Task type | regression | | Total samples | 1105 | | Recommended split | time | | Recommended metric | MAE | ## References <a id="1">[1]</a> ASAP Discovery "ASAP Discovery x OpenADMET Antiviral Drug Discovery Challenge" https://polarishub.io/blog/antiviral-competition <a id="2">[2]</a> Chodera et al. "The ASAP Discovery Antiviral Drug Discovery Challenge" https://doi.org/10.26434/chemrxiv-2025-zd9mr <a id="3">[3]</a> MacDermott-Opeskin, Hugo, et al. "A computational community blind challenge on pan-coronavirus drug discovery data" J. Chem. Inf. Model. 2026, 66, 6, 3129-3149 https://doi.org/10.1021/acs.jcim.5c02106
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