scikit-fingerprints/ASAP_OpenADMET_pIC50_MERS-CoV

--- license: cc0-1.0 task_categories: - tabular-regression - graph-ml - text-classification tags: - chemistry - biology - medical pretty_name: ASAP-OpenADMET pIC50 MERS-CoV size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: "pic50_mers_cov.csv" --- # ASAP-OpenADMET pIC50 MERS-CoV ASAP_OpenADMET_pIC50_MERS-CoV 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 MERS-CoV. | **Characteristic** | **Description** | |:------------------:|:-----------------:| | Tasks | 1 | | Task type | regression | | Total samples | 1198 | | 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