This is the code repo for the paper Prediction of Collision Cross Sections with Graph Convolutional
Network for Metabolites Annotation. We developed a method named GraphCCS which can obtain the data-driven
representations of molecules through the end-to-end learning with GCN, and predict the retention time with
the GCN-learned representations. The network architecture is showed as follow:
We recommend to use conda and pip.
The main packages can be seen in requirements.txt
- Install Anaconda https://www.anaconda.com/
- Install main packages in requirements.txt with following commands
conda create --name GraphCCS python=3.8.13 conda activate GraphCCS python -m pip install -r requirements.txt pip install tqdm
1. Generate adduct ggl-graph of molecules.
GraphCCS is a model for predicting CCS based on graph convolutional networks, so we need to convert SMILES strings to Adduct Graph. The related method is shown in GrapgCCS/dataset.py
mol = Chem.MolFromSmiles(smi)
v_ds = edit_adduct_mol(mol, add)
v_d = fc(mol = v_ds, node_featurizer = node_featurizer, edge_featurizer = None,explicit_hydrogens = True)
Optionnal args
- smi : Molecular SMILES,string
- add : Adduct type of molecule,string
2. Generate the Graph dataset.
data = graph_calculation(dataset)
data_generator = data.DataLoader(data_process_loader_Property_Prediction(data.index.values, data.Label.values,data), **params)
Optionnal args
- dataset : A DataFrame file including SMILES, Adduct Type and experimental CCS values
Train the model based on your own training dataset with train function.
graphccs = Train(train,val,test,**config)
graphccs.train_()
The CCS prediction of the molecule is obtained by feeding the Adduct Graph into the already trained model with Model_prediction function.
model_predict = Predict(dataset,model_path,**config)
Optionnal args
- dataset : A DataFrame file including SMILES, Adduct Type
- model_path: File path where the model is stored