minimol 1.3.5

Molecular fingerprinting using pre-trained deep nets

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, 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:

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 [e-mail]: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.

Performance

The model has been evaluated on 22 benchmarks from the ADMET group of Therapeutics Data Commons (TDC). These are the results when comparing to MolE 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).