SigmaCCS

This is the code base for the paper Ion Mobility Collision Cross Section Prediction Using Structure Included Graph Merged with Adduct. We developed a model named SigmaCCS which can be used to predict CCS of compounds,and a dataset including CCS values of ~90,000,000 molecules from Pubchem was formed.For each molecules,there are "Pubchem ID","SMILES","InChi","Inchikey","Molecular Weight","Exact Mass","Formula"and predicted CCS values of three adduct ion type ([M+H]+,[M-H]-,[M+Na]+). Our paper also uses the GNN-RT.

• sigma.py
• GraphData.py
• model.py
• data Folder:
  • TrainData.csv
  • TestData.csv
  • TestData-pred.csv (Predicted results)
• model Folder:
  • model.h5
• parameter Folder:
  • parameter.pkl

Package required:

We recommend to use conda and pip.

• python3 3.7.7
• rdkit 2020.09.5
• tensorflow 2.4.0
• spektral 1.0.5

By using the requirements/conda/requirements.txt, requirements/pip/requirements.txt file, it will update all your packages to the correct version.

Data pre-processing

SigmaCCS is a model for predicting CCS based on graph neural networks, so we need to convert SMILES strings to Graph. The related method is shown in GraphData.py

1. Generate 3D conformations of molecules.

mol = Chem.MolFromSmiles(smiles)
mol = Chem.AddHs(mol)
ps = AllChem.ETKDGv3()
ps.randomSeed = -1
ps.maxAttempts = 1
ps.numThreads = 0
ps.useRandomCoords = True
re = AllChem.EmbedMultipleConfs(mol, numConfs = 1, params = ps)
re = AllChem.MMFFOptimizeMoleculeConfs(mol, numThreads = 0)

• ETKDGv3 Returns an EmbedParameters object for the ETKDG method - version 3 (macrocycles).
• EmbedMultipleConfs, use distance geometry to obtain multiple sets of coordinates for a molecule.
• MMFFOptimizeMoleculeConfs, uses MMFF to optimize all of a molecule’s conformations

2. Save relevant parameters. For details, seeparameter.py.

• adduct set
• atoms set
• Minimum value in atomic coordinates
• Maximum value in atomic coordinates

3. Generate the Graph dataset. Generate the three matrices used to construct the Graph:
(1) node feature matrix, (2) adjacency matrix, (3) edge feature matrix.

adj, features, edge_features = convertToGraph(smiles, Coordinate, All_Atoms)
DataSet = MyDataset(features, adj, edge_features, ccs)

Optionnal args

• All_Atoms : The set of all elements in the dataset
• Coordinate : Array of coordinates of all molecules
• features : Node feature matrix
• adj : Adjacency matrix
• edge_features : Edge feature matrix

Model training

Train the model based on your own training dataset.

Model_train(ifile, ParameterPath, ofile, ofileDataPath, EPOCHS, BATCHS, Vis, All_Atoms=[], adduct_SET=[])

Optionnal args

• ifile : File path for storing the data of smiles and adduct.
• ofile : File path where the model is stored.
• ParameterPath : Save path of related data parameters.
• ofileDataPath : File path for storing model parameter data.

Predicting CCS

The CCS prediction of the molecule is obtained by inputting Graph and Adduct into the already trained SigmaCCS model.

Model_prediction(ifile, ParameterPath, mfileh5, ofile, Isevaluate = 0)

Optionnal args

• ifile : File path for storing the data of smiles and adduct
• ParameterPath : File path for storing model parameter data
• mfileh5 : File path where the model is stored
• ofile : Path to save ccs prediction values

Usage

The example codes for usage is included in the test.ipynb

Others

The following files are in the others/ folder

• Attribute Analysis.ipynb. analyze the attribute importance
• UMAP.ipynb. visualize the learned representation with UMAP
• UMAPDataset.py. for generating graph datasets.
• theoretical calculation.ipynb. investigate of the relationship between SigmaCCS and theoretical calculation
• Filtering.ipynb. Filtering of target unknown molecules based on the CCS and mz of the molecules
• CFM-ID4. the code for generating MS/MS spectra with CFM-ID 4.0.
• GNN-RT:
  • README.md
• model:
  • model.h5
• data:
  • Attribute importance data
    • Attribute importance (data.csv)
    • Coordinate data (Store the 3D coordinate data of all molecules in data.csv)
  • UMAP data
    • data.csv
    • data_molvec.npy (Molecular vectors of all molecules)
    • data-UMAP-EUC-60.npy
    • Coordinate data (Store the 3D coordinate data of all molecules in data.csv)
  • theoretical calculation data
    • data.csv
    • LJ_data.csv (Get the LJ interaction parameters of different elements according to LJ_data.csv)
    • Coordinate data (Store the 3D coordinate data of all molecules in data.csv)
  • Filtering data
    • data.csv

Package required:

• UMAP 0.5.1

Slurm script

slurm script for generating CCS of PubChem in HPC cluster. The following files are in the slurm/ folder

• mp.py
• multiple_job.sh (Batch generation of slurm script files)
• normal_job.sh (Submit the slurm script for the mp.py file)

Information of maintainer

• zmzhang@csu.edu.cn
• youjiazhang126@163.com
• renfengguo05@foxmail.com