Nonadditivity analysis

Synposis

A program to find key complex patterns in SAR data

Installation

The programm requires python >= 3.10. In your already configured environment, install NonadditivityAnalysis with

pip install nonadditivity

Create python environment with Conda

If you don't have an environment yet, you can use the provided (very minimal) conda environment file to create a valid environment.

conda env create -n <env_name> -f environment.yaml
conda activate <env_name>

or simply

conda create -n <env_name> python=3.*

with * being 10, 11, or 12.

Then use the pip install nonadditivity to install the programm.

Dev Mode

If you want to install the package for development, some extra steps are required.

This package is managed by Poetry, so you first need to install poetry as described here. After that, just clone the repository and install the code with poetry.

$ git clone git+https://github.com/KramerChristian/NonadditivityAnalysis.git
$ cd NonadditivityAnalysis
$ poetry install

How to run the program and get help

The code runs as a simple command-line tool. Command line options are printed via

nonadditivity --help

Example usage

Using the test files supplied, an example run can be

nonadditivity -i <input_file> -d <delimiter> --series-column <series_column_name> -p <property1> -p <property2> ... -u <unit1> -u <unit2>

or with the double-transformation cycles classification

nonadditivity -i <input_file> -d <delimiter> --series-column <series_column_name> -p <property1> -p <property2> ... -u <unit1> -u <unit2> --classify

Input file format

IDENTIFIER [sep] SMILES [sep] property1 ... [sep] series_column(optional) ...

where [sep] is the separator and can be chosen from tab, space, comma, and semicolon.

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Repo Structure

• examples: Contains some example input files.
• nonadditivity/: Contains the source code for the package. See the README in the folder for more info.
• tests: Unit tests for the package.
• environment.yaml: Environment file for the conda environment.
• poetry.lock: File with the specification of libraries used (version and origin).
• pyproject.toml: File containing build instructions for poetry as well as the metadata of the project.

Publication

If you use this code for a publication, please cite Kramer, C. Nonadditivity Analysis. J. Chem. Inf. Model. 2019, 59, 9, 4034–4042. https://pubs.acs.org/doi/10.1021/acs.jcim.9b00631

If you are utilizing the classification module, please cite Guasch, L., Maeder, N., Cumming, J.G., Kramer, C. From mundane to surprising nonadditivity: drivers and impact on ML models. J Comput Aided Mol Des 38, 26 (2024). https://doi.org/10.1007/s10822-024-00566-0

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Background

The overall process is:

1. Parse input:
   • read structures
   • clean and transform activity data
   • remove Salts

2.) Compute MMPs

3.) Find double-transformation cycles

4.) Write to output & calculate statistics

1) Parse input

Ideally, the compounds are already standardized when input into nonadditivity analysis. The code will not correct tautomers and charge state, but it will attempt to desalt the input.

Since Nonadditivity analysis only makes sense on normally distributed data, the input activity data can be transformed depending on the input units. You can choose from "M", "mM", "uM", "nM", "pM", and "noconv". The 'xM' units will be transformed to pActivity with the corresponding factors. 'noconv' keeps the input as is and does not do any transformation.

For multiplicate structures, only the first occurence will be kept.

2) Compute MMPs

Matched Pairs will be computed based on the cleaned structures. This is done by a subprocess call to the external mmpdb program. Per default, 20 parallel jobs are used for the fragmentation. This can be changed on line 681.

3) Find double-transformation cycles

This is the heart of the Nonadditivity algorithm. Here, sets of four compounds that are linked by two transformations are identified. For more details about the interpretation see publication above.

4) Classify double-transformatoin cycles

Runs a bunch of classification functions that calculate topological as well as physico-chemical properties of a double transformation cycle to help you filter out uninteresting cases when analysing the created data. Only runs if --classify is provided in the command line.

5) Write to output and calculate statistics

Information about the compounds making up the cycles and the distribution of nonadditivity is written to output files. [...] denotes the input file name. The file named

"NAA_output.csv"

contains information about the cycles and the Probability distribution

The file named

"perCompound.csv"

contains information about the Nonadditivity aggregated per Compound across all cycles where a given compound occurs.

The file named

"c2c.csv"

links the two files above and can be used for examnple for visualizations in SpotFire.

If you provide the --classify flag in the command line, "NAA_output.csv" and "perCompound.csv" will contain additional columns with the implemented descriptors.

If you provide the --canonicalize flag in the command line, there are two more files genrated.

The first file named

"canonical_na_output.csv"

is like the NAAOutput.csv, but the transformations are canonicalized, i.e. every transformation is only occuring in one way (e.g. only "Cl>>F" and not both "Cl>>F" and "F>>Cl").

The second file named

"canonical_transformations.csv"

contains the transformations included here, so you can build yourself a quasi mmp analysis with this output.

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Copyright

The NonadditivityAnalysis code is copyright 2015-2024 by F. Hoffmann-La Roche Ltd and distributed under Apache 2.0 license (see LICENSE.txt).