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Atom-based machine learning for estimating nucleophilicity and electrophilicity by predicting methyl cation affinities (MCAs) and methyl anion affinities (MAAs), respectively.

TRY IT HERE: https://www.esnuel.org

GitHub repository for our QM-based workflow is: https://github.com/jensengroup/esnuel

GitHub repository for our atom-based descriptor: https://github.com/NicolaiRee/smi2gcs

Installation

Clone this Github repository:

git clone https://github.com/jensengroup/ESNUEL_ML.git

Download and unpack models:

wget -O models.tar.xz https://sid.erda.dk/share_redirect/Ear6g6wl0G; tar xf models.tar.xz; mv models src/esnuelML/.

Download and unpack datasets (OBS! Not required for running the models):

wget -O data.tar.xz https://sid.erda.dk/share_redirect/c7LF5NaYvH; tar xf data.tar.xz

Install the Python environment using conda:

conda env create -f etc/environment.yml && conda activate esnuelML

Finally, download the binaries of xtb version 6.7.0:

mkdir dep; cd dep; wget https://github.com/grimme-lab/xtb/releases/download/v6.7.0/xtb-6.7.0-linux-x86_64.tar.xz; tar -xvf ./xtb-6.7.0-linux-x86_64.tar.xz; cd ..

Usage

An example of usage via CLI command:

python src/esnuelML/predictor.py --smi 'CCOC(=O)CCN(SN(C)C(=O)Oc1cccc2c1OC(C)(C)C2)C(C)C'

The results are saved in a subfolder under "./desc_calcs" with a visual output of the predictions (saved in a .html file).

Raw QM calculation results

All the raw input and output files from the QM calculations can be downloaded here (compressed size: 29 GB, unpacked size: 355 GB):

wget -O calculations.tar.xz https://sid.erda.dk/share_redirect/hp3ttQcTgs; tar xf calculations.tar.xz

Citation

Our work is available as a preprint on ChemRxiv, where more information is available.

@article{ree2024esnuelML,
  title = {Atom-Based Machine Learning for Estimating Nucleophilicity and Electrophilicity with Applications to Retrosynthesis and Chemical Stability},
  url = {http://dx.doi.org/10.26434/chemrxiv-2024-2p7ch},
  DOI = {10.26434/chemrxiv-2024-2p7ch},
  author = {Ree,  Nicolai and Wollschl\"{a}ger,  Jan M. and G\"{o}ller,  Andreas H. and Jensen,  Jan H.},
  year = {2024},
  month = oct 
}