Fully Functionalized Fragment Embeddings

This repository contains a lightweight, pre-trained neural network to obtain 512-dimensional embeddings (vectors) specific to fully functionalized fragments.

The model has been trained on the Fully Functionalized Fragment subsets of the Enamine Stock and Enamine REAL databases, adding up to almost 300,000 compounds.

These embeddings are aimed at capturing graph connectivity properties as well as physicochemical properties of the fully functionalized fragments screened in the Ligand Discovery project.

Installation

git clone https://github.com/ligand-discovery/fragment-embedding.git
cd fragment-embedding
python -m pip install -e .

Please note that installation should be done in development -e mode to ensure that model files (.joblib) are accessible to the package.

Usage

Fragment embeddings can be produced with a simple Python API as follows:

from fragmentembedding import FragmentEmbedder

smiles_list = [
        "C#CCCC1(CCC(=O)N2CCN(Cc3cccc(F)c3)C(=O)C2)N=N1",
        "C#CCCC1(CCC(=O)N2CCN(Cc3cccc(C(F)(F)F)c3)CC2)N=N1",
        "C#CCCC1(CCC(=O)N2CCN3CC(F)(F)C[C@H]3C2)N=N1"
    ]

fe = FragmentEmbedder()
X = fe.transform(smiles_list)

print(X.shape)

The expected shape of X is (3, 512).

About

This work is done by the Georg Winter Lab at CeMM, Vienna.