molnetpack 1.1.10

3DMolMS: prediction of tandem mass spectra from 3D molecular conformations

3DMolMS

(free for academic use)

3D Molecular Network for Mass Spectra Prediction (3DMolMS) is a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. This model's molecular representation, learned through MS/MS prediction tasks, can be further applied to enhance performance in other molecular-related tasks, such as predicting retention times and collision cross sections.

Read our paper in Bioinformatics | Try our online service at GNPS | Install from PyPI

Installation

3DMolMS is available on PyPI. You can install the latest version using pip:

pip install molnetpack

# PyTorch must be installed separately. 
# For CUDA 11.6, install PyTorch with the following command:
pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116

# For CUDA 11.7, use:
pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117

# For CPU-only usage, use:
pip install torch==1.13.0+cpu torchvision==0.14.0+cpu torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cpu

3DMolMS can also be installed through source codes:

git clone https://github.com/JosieHong/3DMolMS.git
cd 3DMolMS

pip install .

Usage

To get started quickly, you can load a CSV or MGF file to predict MS/MS and then plot the predicted results.

import torch
from molnetpack import MolNet

# Set the device to CPU for CPU-only usage:
device = torch.device("cpu")

# For GPU usage, set the device as follows (replace '0' with your desired GPU index):
# gpu_index = 0
# device = torch.device(f"cuda:{gpu_index}")

# Instantiate a MolNet object
molnet_engine = MolNet(device, seed=42) # The random seed can be any integer. 

# Load input data (here we use a CSV file as an example)
molnet_engine.load_data(path_to_test_data='./test/input_msms.csv') # Increasing the batch size if you wanna speed up.
# molnet_engine.load_data(path_to_test_data='./test/input_msms.mgf') # MGF file is also supported
# molnet_engine.load_data(path_to_test_data='./test/input_msms.pkl') # PKL file is faster. 

# Predict MS/MS
spectra1 = molnet_engine.pred_msms(path_to_results='./test/output_qtof_msms.mgf', instrument='qtof')
# You could also download the checkpoint from release and set the 'path_to_checkpoint':
# spectra = molnet_engine.pred_msms(path_to_results='./test/output_msms.mgf', path_to_checkpoint='<path to the checkpoint>')
# Instrument can be 'qtof' or 'orbitrap'. 

# Plot the predicted MS/MS with 3D molecular conformation
molnet_engine.plot_msms(dir_to_img='./img/', instrument='qtof')

For CCS prediction, please use the following codes after instantiating a MolNet object.

# Load input data
molnet_engine.load_data(path_to_test_data='./test/input_ccs.csv')

# Pred CCS
ccs_df = molnet_engine.pred_ccs(path_to_results='./test/output_ccs.csv')

For RT prediction, please use the following code after instantiating a MolNet object. Please note that since this model is trained on the METLIN-SMRT dataset, the predicted retention time is under the same experimental conditions as the METLIN-SMRT set.

# Load input data
molnet_engine.load_data(path_to_test_data='./test/input_rt.csv')

# Pred RT
rt_df = molnet_engine.pred_rt(path_to_results='./test/output_rt.csv')

For saving the molecular embeddings, please use the following codes after instantiating a MolNet object.

# Load input data
molnet_engine.load_data(path_to_test_data='./test/input_savefeat.csv')

# Inference to get the features
features = molnet_engine.save_features()

print('Titles:', ids)
print('Features shape:', features.shape)

The sample input files, a CSV and an MGF, are located at ./test/demo_input.csv and ./test/demo_input.mgf, respectively. If the input data is only expected to be used in CCS prediction, you may assign an arbitrary numerical value to the Collision_Energy field in the CSV file or to COLLISION_ENERGY in the MGF file. It's important to note that during the data loading phase, any input formats that are not supported will be automatically excluded. Below is a table outlining the types of input data that are supported:

Item	Supported input
Atom number	<=300
Atom types	'C', 'O', 'N', 'H', 'P', 'S', 'F', 'Cl', 'B', 'Br', 'I', 'Na'
Precursor types	'[M+H]+', '[M-H]-', '[M+H-H2O]+', '[M+Na]+', '[M+2H]2+'
Collision energy	any number

The documents for running MS/MS prediction from source codes are at MSMS_PRED.md.

Citation

If you use 3DMolMS in your research, please cite our paper:

@article{hong20233dmolms,
  title={3DMolMS: prediction of tandem mass spectra from 3D molecular conformations},
  author={Hong, Yuhui and Li, Sujun and Welch, Christopher J and Tichy, Shane and Ye, Yuzhen and Tang, Haixu},
  journal={Bioinformatics},
  volume={39},
  number={6},
  pages={btad354},
  year={2023},
  publisher={Oxford University Press}
}
@article{hong2024enhanced,
  title={Enhanced structure-based prediction of chiral stationary phases for chromatographic enantioseparation from 3D molecular conformations},
  author={Hong, Yuhui and Welch, Christopher J and Piras, Patrick and Tang, Haixu},
  journal={Analytical Chemistry},
  volume={96},
  number={6},
  pages={2351--2359},
  year={2024},
  publisher={ACS Publications}
}

Thank you for considering 3DMolMS for your research needs!

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.