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https://github.com/idslme/idsl_mint
A Deep Learning Framework to Interpret Raw Mass Spectrometry (m/z) Data
https://github.com/idslme/idsl_mint
cheminformatics lipidomics mass-spectrometry metabolomics molecular-fingerprints msms python3 pytorch rdkit small-molecule transformer untargeted-metabolomics
Last synced: about 1 month ago
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A Deep Learning Framework to Interpret Raw Mass Spectrometry (m/z) Data
- Host: GitHub
- URL: https://github.com/idslme/idsl_mint
- Owner: idslme
- Created: 2023-07-11T15:31:33.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-21T01:06:48.000Z (11 months ago)
- Last Synced: 2024-01-22T01:45:35.817Z (11 months ago)
- Topics: cheminformatics, lipidomics, mass-spectrometry, metabolomics, molecular-fingerprints, msms, python3, pytorch, rdkit, small-molecule, transformer, untargeted-metabolomics
- Language: Python
- Homepage:
- Size: 972 KB
- Stars: 4
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# IDSL_MINT
[](https://github.com/sajfb)
[](https://www.rdkit.org/)
[](https://img.shields.io/pypi/pyversions/d3blocks)
[](https://github.com/pytorch)
**IDSL_MINT: Mass spectra INTerpretation** by the [**Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.ME)**](https://www.idsl.me) is a transformative mass spectrometry data processing framework. This innovative approach for mass spectrometry data processing has been constructed upon the transformer models delineated in the seminal paper, [*'Attention is all you need'*](https://arxiv.org/abs/1706.03762). **IDSL_MINT** has been meticulously engineered to predict molecular fingerprint descriptors and structures from MS/MS spectra in addition to forecasting MS/MS spectra from canonical SMILES. A key distinguishing feature of **IDSL_MINT** is its compatibility with any reference MS/MS data in ***.msp*** format to tailor **IDSL_MINT** models for various applications.
## Table of Contents
- [Features of IDSL_MINT](https://github.com/idslme/idsl_mint#features-of-idsl_mint)
- [Installation](https://github.com/idslme/idsl_mint#installation)
- [Workflow](https://github.com/idslme/idsl_mint#workflow)
- [IDSL_MINT: Translating MS/MS Spectra into Molecular Fingerprints](https://github.com/idslme/idsl_mint#idsl_mint-translating-msms-spectra-into-molecular-fingerprints)
- [IDSL_MINT: Translating MS/MS Spectra into Canonical SMILES](https://github.com/idslme/idsl_mint#idsl_mint-translating-msms-spectra-into-canonical-smiles)
- [IDSL_MINT: Transforming Fingerprints into MS/MS Fragments](https://github.com/idslme/idsl_mint#idsl_mint-transforming-fingerprints-into-msms-fragments)
- [Citation](https://github.com/idslme/idsl_mint#citation)
## Features of IDSL_MINT
1) Parameter selection for training and prediction through user-friendly and well-documented [**YAML** files](https://github.com/idslme/IDSL_MINT/tree/main/YAML)
2) Compatibility with *.msp* file formats.
3) Compatibility with various fingerprint descriptor methods.
4) Supports beam search inferencing.
5) Utilizes the power of the transformer model architecture.
6) Device-agnostic processing.
## Installation
1. Installation of Prerequisites:
a. Install [PyTorch](https://pytorch.org/get-started/locally) according to your system configurations. **IDSL_MINT** is device-agnostic and fully supports `cuda` GPU processing.
b. Install [RDKit](https://www.rdkit.org/docs/Install.html).
2. Install the package:
2.1. Option 1: `pip`
- `pip install git+https://github.com/idslme/IDSL_MINT`
- `pip install IDSL_MINT`
2.2. Option 2: `conda`
- `git clone https://github.com/idslme/IDSL_MINT.git`
- `cd IDSL_MINT`
- `conda env create -f environment.yml`
- `conda activate IDSL_MINT`
- `pip install -e .`
3. Update the Python PATH:
`export PATH="root/.local/bin:$PATH"` --> root directory should be your system root directory.
## Workflow
The **IDSL_MINT** framework encapsulates three transformative approaches to deeply interpret mass spectrometry data. Each of these methodologies can be effectively managed using designated model configuration `yaml` files. In the training step, weights of **IDSL_MINT** models are stored and updated in a designated directory on the decreasing trajectory of the training loss value to ensure optimal performance and accuracy. The [`yaml`](https://github.com/idslme/IDSL_MINT/tree/main/YAML) files are easy to update and model configuration is significantly simplified and commented. After configuring the model in the designated `yaml` file, run the below bash command to perform calculations. The **IDSL_MINT** package can automatically detect types of `yaml` file to run training or inference operations.
MINT_workflow --yaml /path/to/yaml/file
#### Important tips:
- **IDSL_MINT** can extract information from `comment: ` and `comments: ` entries in ***.msp*** files which enables this platform to process MoNA, GNPS, and other public library with any pre-treatment requirements.
- **IDSL_MINT** identifies chemical structures through `SMILES: ` or `InChI: ` labels without case sensitivity.
- In case multiple similar headers are present in a MSP block, the one with the longest content is selected for parsing.
- MSP blocks must include `PrecursorMZ: ` row entries.
## IDSL_MINT: Translating MS/MS Spectra into Molecular Fingerprints
[](https://colab.research.google.com/drive/16A-Hw6S_04nxlopp7yefZkVB5Aakcodu#scrollTo=E4o1pG-tZNDR)
**IDSL_MINT** includes a method to translate MS/MS spectra into molecular fingerprint descriptors. This method offers the option to calculate fingerprints using the [Extended-connectivity fingerprints (ECFPs)](https://doi.org/10.1021/ci100050t) or [MACCS Keys](https://doi.org/10.1021/ci200081k) RDKit methods from InChI and SMILES row entries. Another option to obtain molecular fingerprints is to parse the MSP files for the user-provided fingerprints. The following is an example of an Aspirin MSP block with custom fingerprint bits.
Name: Aspirin
Fingerprint: 15-53-85-157-246-322-329-343-444-464-553-708-763-785-799-821-847-1040-1139-1240-1250-1317-1348-1439-1450-1460-1475-1479-1502-1674-1693-1734-1841-1866-2046-2310-2329-2413-2627-2750-2755-2777-2782-2799-2901-2911-2915-3028-3049-3394-3412-3442-3514-3535-3557-3700-3737-3785-3972-3996
Synon: Acetyl salicilic acid
Synon: 2-acetyloxybenzoic acid
InChI: InChI=1S/C9H8O4/c1-6(10)13-8-5-3-2-4-7(8)9(11)12/h2-5H,1H3,(H,11,12)
Precursor_type: [M+H]+
Spectrum_type: MS2
PrecursorMZ: 181.0495
Instrument_type: LC-ESI-QFT
Instrument: Q Exactive Plus Orbitrap Thermo Scientific
Ion_mode: P
Collision_energy: 15 (nominal)
Formula: C9H8O4
MW: 180
ExactMass: 180.042258736
Num Peaks: 10
65.0385 0.217327
76.0304 0.107699
77.0383 0.124517
92.0255 0.129908
121.0283 0.125197
133.028 0.149192
149.0231 100.000000
163.0386 63.824575
167.0337 0.261816
181.0493 0.613766
`Fingerprint` row entries may be in any line in MSP blocks between `Name` and `Num Peaks` rows, and fingerprint bits must be dash-separated. This example represented Avalon fingerprint bits with `nBits = 4096` for Aspirin MS/MS spectra.
To train an **IDSL_MINT** model with molecular fingerprint descriptors, download and fill a [MINT_MS2FP_trainer.yaml](https://github.com/idslme/IDSL_MINT/tree/main/YAML/MINT_MS2FP_trainer.yaml) file. Similarly, for model prediction, use [MINT_MS2FP_predictor.yaml](https://github.com/idslme/IDSL_MINT/tree/main/YAML/MINT_MS2FP_predictor.yaml) file.
A [colab notebook](https://colab.research.google.com/drive/16A-Hw6S_04nxlopp7yefZkVB5Aakcodu#scrollTo=E4o1pG-tZNDR) was presented to demonstrate the performance of **IDSL_MINT** in training and predicting molecular fingerprint descriptors using MS/MS data.
## IDSL_MINT: Translating MS/MS Spectra into Canonical SMILES
[](https://colab.research.google.com/drive/1UUZwCpI4b0adHZ3y4JTRDPlin-KAWIvQ#scrollTo=RbAS-ZFPVOqM)
In this approach, InChI and SMILES row entries in the MSP blocks are converted into canonical SMILES using [RDKit](https://www.rdkit.org). Next, standard canonical SMILES are tokenized using a method similar to [RXNFP](https://rxn4chemistry.github.io/rxnfp). As long as InChI and SMILES row entries present in the MSP blocks are available, this approach may be used to train an **IDSL_MINT** model.
To train an **IDSL_MINT** model to predict molecular structures from MS/MS spectra, download and fill a [MINT_MS2SMILES_trainer.yaml](https://github.com/idslme/IDSL_MINT/tree/main/YAML/MINT_MS2SMILES_trainer.yaml) file. Likewise, for model prediction, use [MINT_MS2SMILES_predictor.yaml](https://github.com/idslme/IDSL_MINT/tree/main/YAML/MINT_MS2SMILES_predictor.yaml) file.
A [colab notebook](https://colab.research.google.com/drive/1UUZwCpI4b0adHZ3y4JTRDPlin-KAWIvQ#scrollTo=RbAS-ZFPVOqM) was presented to demonstrate the performance of **IDSL_MINT** in training and predicting canonical SMILES using MS/MS data.
## IDSL_MINT: Transforming Fingerprints into MS/MS Fragments
This method is designed to translate fingerprints into MS/MS fragments using a transformer model. This approach contrasts with previous methods that predict fragment mass from fingerprints.
To train an **IDSL_MINT** model to predict MS/MS spectra from molecular structures, download and fill a [MINT_FP2MS_trainer.yaml](https://github.com/idslme/IDSL_MINT/tree/main/YAML/MINT_FP2MS_trainer.yaml) file. Likewise, for model prediction, use [MINT_FP2MS_predictor.yaml](https://github.com/idslme/IDSL_MINT/tree/main/YAML/MINT_FP2MS_predictor.yaml) file.
## Citation
[1] Fakouri Baygi, S., Barupal, D.K. [IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra](https://doi.org/10.1186/s13321-024-00804-5). *Journal of Cheminformatics*, **2024**, *16(8)*.