Nmrfilter v1.5 is an updated version of Nmrfilter, originally developed by S. Kuhn, S. Colreavy-Donnelly, J. Santana de Souza and R. M. Borges to demonstrate their improved software pipeline for NMR mixture analysis.

Nmrfilter v1.5 can be used to find the most likely candidate substance from a set of candidates provided by there user, thereby automatically identifying the substance from its NMR spectrum data. Nmrfilter v1.5 outputs a list of the provided candidate substances and their individual match ratings in comparison to the original substance with plots visualizing the similarities between the compounds.

Installation

Requirements are Java and Python. For Java, version 1.8 or higher is needed. A JRE (Java Runtime Environment) is enough, a JDK is not required.

Python must be version >= 3.10.2. (3.10.2 an 3.11.2 have been tested and used).

Nothing further is needed, as the program creates a Python virtual environment installid all the needed packages.

Anaconda

There are 2 Anaconda environments available for use:

nmrfilter for running the program using the CPU

Install by running conda env create -f environment-cpu.yml

nmrfiltergpu for running the program using the GPU Install by running conda env create -f environment-gpu.yml

Activate the environments by running conda activate <environment name>

When using the Anaconda, the main run-script will take care of the unnecessary nesting.

Use of Respredict

Respredict is another way of predicting NMR spectral properties using machine learning. Possibly yielding better results, respredict is available for use in Nmrfilter v1.5. It can be enabled in the properties file, see Glossary.

Use of Respredict requires extra packages, rendering the use of bundled Anaconda environments necessary. See Anaconda for further information.

Jupyter Notebook

Jupyter Notebook must be installed locally when to be used. Jupyter Notebook can be enabled by first running:

jupyter nbextension enable --py widgetsnbextension

Running

Nmrfilter v1.5 works on projects, where each project is a folder. Example projects can be found here. The folder must contain the following files:

A list of candidate structures in the form of SMILES, one structure per line. File name can be configured by the msmsinput property.
Measured spectrum data in a .csv file. The file must be a list of shifts, coordinates seperated by a tab. ¹³C shift in the first dimension, ¹H shift in the second. Each row corresponds to one shift. HMBC and HSQC shifts should be included. File name can be configured by the spectruminput property.

With projects in place, Nmrfilter v1.5 will produce an output ranking the candidates.

Before running, in the nmrproc.properties file, the datadir property must be set to the absolute path of the folder containing project folders. Additionally, in the nmrproc.properties select the right solvent used in the mixture. See Glossary for available options. NB! Each project can have their own properties file whose properties will be prioritized over the default program file.

With these files in place, the program can be run by using ./nmrfilter.sh <project name> on Linux or ./nmrfilter.bat <project name> on Windows. Replace <project name> with the name of the project (folder name) you want the predictions for. When running, the program creates a Python virtual environment installing everything needed for its use. If you prefer to use the external environment, use an Anaconda environment provided.

Following features are available:

HSQCTOCSY shifts can be included. Include the shifts in the spectruminput file and set the usehsqctocsy property to true.
Debug output can be produced by setting the property debug to true. As a prequisite, a file testallnames.txt needs to be included in the project folder. The file should contain the names of the compounds in the same order as in the msmsinput file.
Parameters for tolerances and resolutions for the clustering and network algorithm can be set. See Glossary for details.
To use respredict, set the property usedeeplearning to true. See Use of Respredict for further information.
Nmrfilter v1.5 uses a offline reference database for the HOSE code based spectra predictions. The database can be updated by running ./nmrfilter.sh --update. The database gets updated on a monthly basis.
Nmrfilter v1.5 can be used for batch simulation of desired NMR spectra. Using the --simulate flag when running, e.g ./nmrfilter.sh pbolduswithhsqctocsy --simulate, Nmrfilter v1.5 will simulate spectras for the candidates. testallnames.txt file is mandatory (may be placeholder names) together with the msmsinput SMILES file. The program will produce spectra for the experiments specified in nmrproc.properties. These can be found at <project-folder>/sim_plots.

NB! While the file testallnames.txt is not mandatory for the identification, it is highly recommended to include it. Without this file, no plots will be produced for further manual analysis/verification of results.

See Glossary for information about all the configurable properties.

Glossary

The following table contains all the properties in the nmrproc.properties file with explanations, options and default values shown.

Property	Description	Default
datadir	Path to the absolute directory containing project folders to be used for input.	/home/karl/nmrfilterprojects
msmsinput	Name of the file containing the list of can-didate substances in a project folder.	testall.smi
predictionoutput	Name of the file containing simulated spectra shifts of the candidate substances .	resultprediction.csv
result	File name of the generated result file	result.txt
solvent	Name of solvent if used. Choices available are `Methanol-D4 (CD3OD)`, `Chloro-form-D1 (CDC13)` and `Dimethylsulph-oxide-D6 (DMSO-D6, C2D6SO)`. Otherwise use `Unreported`.	`Methanol-D4 (CD3OD)`
tolerancec	Tolerance for the 13C axis.	0.2
toleranceh	Tolerance for the 1H axis.	0.02
spectuminput	Name of the file containing measured spectrum data.	realspectrum.csv
clusteringoutput	Name of a file created containing initial found cross peaks.	cluster.txt
rberresolution	Resolution parameter for the RBER algo-rithm provided by the Louvain library, which changes the size of the clusters. Larger the value, smaller the clusters.	0.2
usehmbc	Boolean. Define the use of HMBC or not.	true
dotwobonds	Changes the HOSE code prediction to explore 2 spheres instead of the default 3.	false
usedeeplearning	Boolean. If set “true”, uses respredict prediction instead of a HOSE code based one.	false
debug	Produces debug output, e.g measured unused peaks plotted, additional files inside the project folder.	false
labelsimulated	If `true`, plots show additional shift-atom tie information.	false
hmbcbruker	Optional. Used for plot backgrounds. HMBC Bruker folder information (must be inside the project folder). Format `<folder-name>,<pdata folder name>`	`NaN`
hsqcbruker	Optional. Used for plot backgrounds. HSQC Bruker folder information (must be inside the project folder). Format `<folder-name>,<pdata folder name>`	`NaN`
hsqctocsybruker	Optional. Used for plot backgrounds. HSQCTOCSY Bruker folder information (must be inside the project folder). Format `<folder-name>,<pdata folder name>`	`NaN`