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license: cc0-1.0
task_categories:
- tabular-regression
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
pretty_name: ASAP-OpenADMET MLM
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: "mlm.csv"
---
# ASAP-OpenADMET MLM
ASAP_OpenADMET_MLM 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 MLM (mouse liver microsomal intrinsic clearance in uL/min/mg) of molecules.
| **Characteristic** | **Description** |
|:------------------:|:---------------:|
| Tasks | 1 |
| Task type | regression |
| Total samples | 425 |
| 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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