This repository (imported from https://github.com/skinnider/NPS-generation.git; Citation: Skinnider, M.A., Wang, F., Pasin, D. et al. A deep generative model enables automated structure elucidation of novel psychoactive substances. Nat Mach Intell 3, 973–984 (2021). https://doi.org/10.1038/s42256-021-00407-x) was used to generate natural product(NP)-like molecules by learning the molecular language of ~400,000 known natural product molecules found in the COCONUT(https://coconut.naturalproducts.net) database.
The results of the experiment and the description of the data generated are presented in the manuscript titled "67 million natural product-like compound database generated via molecular language processing" (DOI: https://doi.org/10.1038/s41597-023-02207-x)
NPG is defined as the root directory of GitHub Repository "https://github.com/SIBERanalytics/Natural-Product-Generator"
python NPG/python/augment-SMILES.py --input_file='/path/to/smiles/coconut_smiles_nostereo_sample80.smi' --output_file='/path/to/augmentedsmiles/coconut_augsmiles.smi' --enum_factor=10 > augment-SMILES.out
python NPG/python/train_model.py --smiles_file='/path/to/augmentedsmiles/coconut_augsmiles.smi' --output_dir='/path/to/model' --rnn_type='LSTM' --sample_size=100000000 --patience=10000 > train_model.out
Note: Output will be written as sampled-SMILES-$(sample_idx).smi. We are sampling 1000 more SMILES in the following example.
python NPG/python/sample_molecules.py --model_file='/path/to/augmentedsmiles/coconut_rnn_model.pt --smiles_file='/path/to/augmentedsmiles/coconut_augsmiles.smi' --mols_per_file=1000 --output_dir=/path/to/output_smiles --sample_idx=1 > sample_molecules.out
For more advanced options (not used in this study), please visit https://github.com/skinnider/NPS-generation.git
Figshare link to data for training of LSTM model will be provided upon publication of manuscript.
The python packages in the conda environment for running the experiment are identical to that of https://github.com/skinnider/NPS-generation.git. Yaml file is provided as environment.yml
# Name Version Build Channel
_libgcc_mutex 0.1 main
beautifulsoup4 4.9.3 pyhb0f4dca_0
blas 1.0 mkl
brotlipy 0.7.0 py36h27cfd23_1003
bzip2 1.0.8 h7b6447c_0
ca-certificates 2021.4.13 h06a4308_1
cairo 1.14.12 h8948797_3
certifi 2020.12.5 py36h06a4308_0
cffi 1.14.0 py36h2e261b9_0
chardet 3.0.4 py36h06a4308_1003
conda 4.9.2 py36h06a4308_0
conda-build 3.20.5 py36_1
conda-package-handling 1.7.2 py36h03888b9_0
cryptography 3.3.1 py36h3c74f83_0
cudatoolkit 10.0.130 0
cudnn 7.6.5 cuda10.0_0
deepsmiles 1.0.1 pypi_0 pypi
et_xmlfile 1.0.1 py_1001
fcd-torch 1.0.7 pypi_0 pypi
filelock 3.0.12 py_0
fontconfig 2.13.0 h9420a91_0
freetype 2.10.4 h5ab3b9f_0
glib 2.63.1 h5a9c865_0
glob2 0.7 py_0
icu 58.2 he6710b0_3
idna 2.10 py_0
intel-openmp 2020.2 254
jdcal 1.4.1 py_0
jinja2 2.11.2 py_0
jpeg 9b h024ee3a_2
lcms2 2.11 h396b838_0
ld_impl_linux-64 2.33.1 h53a641e_7
libarchive 3.4.2 h62408e4_0
libboost 1.73.0 h37e3b65_11
libedit 3.1.20191231 h14c3975_1
libffi 3.2.1 hf484d3e_1007
libgcc-ng 9.1.0 hdf63c60_0
libgfortran-ng 7.3.0 hdf63c60_0
liblief 0.10.1 he6710b0_0
libpng 1.6.37 hbc83047_0
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.1.0 h2733197_1
libuuid 1.0.3 h1bed415_2
libxcb 1.14 h7b6447c_0
libxml2 2.9.10 hb55368b_3
lz4-c 1.9.2 heb0550a_3
markupsafe 1.1.1 py36h7b6447c_0
mkl 2020.2 256
mkl-service 2.3.0 py36he8ac12f_0
mkl_fft 1.2.0 py36h23d657b_0
mkl_random 1.1.1 py36h0573a6f_0
ncurses 6.2 he6710b0_1
ninja 1.10.2 py36hff7bd54_0
numpy 1.19.2 py36h54aff64_0
numpy-base 1.19.2 py36hfa32c7d_0
olefile 0.46 py_0
openpyxl 3.0.7 pyhd3eb1b0_0
openssl 1.1.1k h27cfd23_0
pandas 1.1.3 py36he6710b0_0
patchelf 0.12 h2531618_1
pcre 8.44 he6710b0_0
pillow 8.0.1 py36he98fc37_0
pip 20.3.1 py36h06a4308_0
pixman 0.40.0 h7b6447c_0
pkginfo 1.6.1 py36h06a4308_0
psutil 5.7.2 py36h7b6447c_0
py-boost 1.73.0 py36h962f231_11
py-lief 0.10.1 py36h403a769_0
pycosat 0.6.3 py36h27cfd23_0
pycparser 2.20 py_2
pyopenssl 20.0.0 pyhd3eb1b0_1
pysocks 1.7.1 py36h06a4308_0
python 3.6.10 h191fe78_1
python-dateutil 2.8.1 py_0
python-libarchive-c 2.9 py_0
pytorch 1.1.0 cuda100py36he554f03_0
pytz 2020.4 pyhd3eb1b0_0
pyyaml 5.3.1 py36h7b6447c_1
rdkit 2020.09.1.0 py36hd50e099_1 rdkit
readline 7.0 ha6073c6_4
requests 2.25.0 pyhd3eb1b0_0
ripgrep 12.1.1 0
ruamel_yaml 0.15.87 py36h7b6447c_1
scipy 1.5.2 py36h0b6359f_0
selfies 1.0.2 pypi_0 pypi
setuptools 51.0.0 py36h06a4308_2
six 1.15.0 py36h06a4308_0
soupsieve 2.0.1 py_0
sqlite 3.33.0 h62c20be_0
tk 8.6.10 hbc83047_0
tqdm 4.54.1 pyhd3eb1b0_0
urllib3 1.25.11 py_0
wheel 0.36.1 pyhd3eb1b0_0
xlrd 2.0.1 pyhd3eb1b0_0
xz 5.2.5 h7b6447c_0
yaml 0.2.5 h7b6447c_0
zlib 1.2.11 h7b6447c_3
zstd 1.4.5 h9ceee32_0