https://github.com/graphcore-research/minimol
MiniMol is a 10M-parameters molecular fingerprinting model pre-trained on >3300 biological and quantum tasks
https://github.com/graphcore-research/minimol
Last synced: 5 months ago
JSON representation
MiniMol is a 10M-parameters molecular fingerprinting model pre-trained on >3300 biological and quantum tasks
- Host: GitHub
- URL: https://github.com/graphcore-research/minimol
- Owner: graphcore-research
- License: mit
- Created: 2024-07-10T08:12:53.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-29T14:21:46.000Z (about 1 year ago)
- Last Synced: 2025-09-30T04:54:17.115Z (9 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 6.1 MB
- Stars: 25
- Watchers: 5
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
A parameter-efficient molecular featuriser that generalises well to biological tasks thanks to the effective pre-training on biological and quantum mechnical datasets.
The model has been introduced in the paper [𝙼𝚒𝚗𝚒𝙼𝚘𝚕: A Parameter-Efficient Foundation Model for Molecular Learning](https://arxiv.org/abs/2404.14986), published in the ICML workshop on *Accessible and Efficient Foundation Models for Biological Discovery* in 2024.
## Usage
Embeddings can be generated in four lines of code:
```
from minimol import Minimol
model = Minimol()
smiles = [
'COc1ccc2cc(C(=O)NC3(C(=O)N[C@H](Cc4ccccc4)C(=O)NCC4CCN(CC5CCOCC5)CC4)CCCC3)sc2c1',
'Nc1nc(=O)c2c([nH]1)NCC(CNc1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1)N2C=O',
'O=C1CCCN1CCCCN1CCN(c2cc(C(F)(F)F)ccn2)CC1',
'c1ccc(-c2cccnc2)cc1',
]
model(smiles)
>> A list of 4 tensors of (512,) shape
```
For a Colab notebook showing how to use Minimol's fingerprints to achieve SoTA results on a downstream task, click here: [](https://githubtocolab.com/graphcore-research/minimol/blob/main/notebooks/downstream_adaptation.ipynb)
## Installation
### Pip
When used with cuda, use `nvcc --version` to see which version of the driver is installed on your machine, to select the wheel (cuXXX):
```
pip install torch-sparse torch-cluster torch-scatter -f https://pytorch-geometric.com/whl/torch-2.3.0+cu124.html
pip install minimol
```
### Local
```
git clone git@github.com:graphcore-research/minimol.git
cd minimol
mamba env create -f env.yml -n minimol_venv
mamba activate minimol
```
*To install mamba see [the official documentation](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html).*
## Performance
The model has been evaluated on 22 benchmarks from the ADMET group of [Therapeutics Data Commons (TDC)](https://tdcommons.ai). These are the results when comparing to [MolE](https://arxiv.org/abs/2211.02657) and TOP5 models from the TDC leaderboard (as of June 2024):
| TDC Dataset | | | TDC Leaderboard | MolE | | MiniMol (GINE)| |
|----------------------|----------|------------|-----------------|----------------|----------|---------------|----------|
| **Name** | **Size** | **Metric** | **SoTA Result** | **Result** | **Rank** | **Result** | **Rank** |
| **Absorption** | | | | | | | |
| Caco2 Wang | 906 | MAE | 0.276 ± 0.005 | 0.310 ± 0.010 | 6 | 0.350 ± 0.018 | 7 |
| Bioavailability Ma | 640 | AUROC | 0.748 ± 0.033 | 0.654 ± 0.028 | 7 | 0.689 ± 0.020 | 5 |
| Lipophilicity AZ | 4,200 | MAE | 0.467 ± 0.006 | 0.469 ± 0.009 | 3 | 0.456 ± 0.008 | 1 |
| Solubility AqSolDB | 9,982 | MAE | 0.761 ± 0.025 | 0.792 ± 0.005 | 5 | 0.741 ± 0.013 | 1 |
| HIA Hou | 578 | AUROC | 0.989 ± 0.001 | 0.963 ± 0.019 | 7 | 0.993 ± 0.005 | 1 |
| Pgp Broccatelli | 1,212 | AUROC | 0.938 ± 0.002 | 0.915 ± 0.005 | 7 | 0.942 ± 0.002 | 1 |
| **Distribution** | | | | | | | |
| BBB Martins | 1,975 | AUROC | 0.916 ± 0.001 | 0.903 ± 0.005 | 7 | 0.924 ± 0.003 | 1 |
| PPBR AZ | 1,797 | MAE | 7.526 ± 0.106 | 8.073 ± 0.335 | 6 | 7.696 ± 0.125 | 4 |
| VDss Lombardo | 1,130 | Spearman | 0.713 ± 0.007 | 0.654 ± 0.031 | 3 | 0.535 ± 0.027 | 7 |
| **Metabolism** | | | | | | | |
| CYP2C9 Veith | 12,092 | AUPRC | 0.859 ± 0.001 | 0.801 ± 0.003 | 5 | 0.823 ± 0.006 | 4 |
| CYP2D6 Veith | 13,130 | AUPRC | 0.790 ± 0.001 | 0.682 ± 0.008 | 6 | 0.719 ± 0.004 | 5 |
| CYP3A4 Veith | 12,328 | AUPRC | 0.916 ± 0.000 | 0.867 ± 0.003 | 7 | 0.877 ± 0.001 | 4 |
| CYP2C9 Substrate | 666 | AUPRC | 0.441 ± 0.033 | 0.446 ± 0.062 | 2 | 0.474 ± 0.025 | 1 |
| CYP2D6 Substrate | 664 | AUPRC | 0.736 ± 0.024 | 0.699 ± 0.018 | 7 | 0.695 ± 0.032 | 6 |
| CYP3A4 Substrate | 667 | AUROC | 0.662 ± 0.031 | 0.670 ± 0.018 | 1 | 0.663 ± 0.008 | 2 |
| **Excretion** | | | | | | | |
| Half Life Obach | 667 | Spearman | 0.562 ± 0.008 | 0.549 ± 0.024 | 4 | 0.495 ± 0.042 | 6 |
| Clearance Hepatocyte | 1,102 | Spearman | 0.498 ± 0.009 | 0.381 ± 0.038 | 7 | 0.446 ± 0.029 | 3 |
| Clearance Microsome | 1,020 | Spearman | 0.630 ± 0.010 | 0.607 ± 0.027 | 6 | 0.628 ± 0.005 | 2 |
| **Toxicity** | | | | | | | |
| LD50 Zhu | 7,385 | MAE | 0.552 ± 0.009 | 0.823 ± 0.019 | 7 | 0.585 ± 0.005 | 2 |
| hERG | 648 | AUROC | 0.880 ± 0.002 | 0.813 ± 0.009 | 7 | 0.846 ± 0.016 | 4 |
| Ames | 7,255 | AUROC | 0.871 ± 0.002 | 0.883 ± 0.005 | 1 | 0.849 ± 0.004 | 5 |
| DILI | 475 | AUROC | 0.925 ± 0.005 | 0.577 ± 0.021 | 7 | 0.956 ± 0.006 | 1 |
| | | | | **Mean Rank:** | 5.2 | | 3.3 |
## License
Copyright (c) 2024 Graphcore Ltd. Licensed under the MIT License.
The included code is released under the MIT license (see [details of the license](LICENSE)).