---
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