This is the accompanying Repository for our work on "Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks." https://doi.org/10.1016/j.csbj.2021.07.032
The code can be used to reproduce our results, retrain the models and compute fingerprints for your own SMILES
-string.
-
Download the Repository
-
Download the data at ...(only requiered for reproduction of experiments)
we are still dealing with the University Hosting Service, in the mean time send an e-mail to janosch.menke [at] wwu.de We will provide an personalized Download link
- extract the data.zip file into the same repository
|- data |- neural_npfp |- results |- settings
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Create and Activate Conda Environment: Navigate to the Folder containing the environment.yml
conda env create -f environment.yml conda activate neural_npfp_env
Please install Pyorch with you appropriate choice of cuda.
conda install pytorch==1.7.0 cudatoolkit=*Your Version* -c pytorch
The data
folder contains multiple datasets in case you are interested in just the dataset consisting of the NP and synthetic compounds from ZINC please use the data/coconut_synthetic.csv
The validation data collected by us can be found in data/validation_sets/np_target_identification/
fps_targets
contain the precomputed ECFP for each targetsmiles_targets
contains the SMILES.
The clean_task1.csv
and clean_task2.csv
were not created by us, we did not include the SMILES for those compounds. If you are interested in the actual compounds we refer to the original publication.
Seo, M.; Shin, H.K.; Myung, Y. et al. Development of Natural Compound Molecular Fingerprint (NC-MFP) with the Dictionary of Natural Products (DNP) for natural product-based drug development. Journal of Cheminformatics. 2020, 12(6) https://doi.org/10.1186/s13321-020-0410-3
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Sterling, T.; Irwin, J. J. ZINC 15–ligand Discovery for Everyone. Journal of Chemical Information and Modeling. 2015, 55, 2324–2337.
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Sorokina, M.; Merseburger, P.; Rajan, K.; Yirik, M. A.; Stein-beck, C. COCONUT online: Collection of Open Natural Products Database. Journal of Cheminformatics 2021, 13, 1–13.
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Mendez, D. et al. ChEMBL: Towards Direct Deposition of Bioassay Data. Nucleic Acids Research. 2018, 47, D930–D940.
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Zeng, X.; Zhang, P.; He, W.; Qin, C.; Chen, S.; Tao, L.;Wang, Y.; Tan, Y.; Gao, D.; Wang, B.; Chen, Z.; Chen, W.;Jiang, Y. Y.; Chen, Y. Z. NPASS: Natural Product Activity and Species Source Database for Natural Product Research, Discovery and Tool Development. Nucleic Acids Research. 2018, 46, D1217–D1222.
To reproduce the results, please run:
python experiment.py
This will train the models again. The hyperparameters used can be found in settings/settings.yml
You can also change the hyperparameters. We recommend creating a new settings.yml
If you want to use your own settings.yml
please run.
The trained models will be saved in data/trained_models/*folder name specified in settings.yml*
python experiment.py --input *path to your settings.yml*
After training the results
script will perfrom the similarity search and reproduce some of the graphics used.
python results.py
In results/plots
the plots and some addtionall files will be saved.
In case you want to evaluate models that you trained with experiment.py
but a different settings.yml
Run:
python results --input *path to the folder containing the models*
You can use a csv file containing a column with SMILES strings as input to our model.
Naviagte to *your path*/neural_npfp/neural_npfp
and run:
python get_fp.py ../data/testdata.csv -s smiles
smiles
refers to the name of the column containing the SMILES strings.
By default the fingerprints using the NP_AUX model. If you want to use a different model use the flag -m
followed by either ae
, aux
or base
.
python get_fp.py ../data/testdata.csv -s smiles
You can also provide the Index of the column containing the SMILES.
python get_fp.py ../data/testdata.csv -s 0
If you do not have a header add the -n
flag
python get_fp.py ../data/testdata.csv -s 0 -n
If you want to use the NNFPs for a similarity search make sure that the query is in the same file as the molecules you want to screen before you generate the Fingerprints.
With python simsearch.py *path of fingerprintfile* -q *index of the query*
the similairty search can be performed
Given you generated fingerprints for the testdata.csv
. The following code will perform a similarity search for the query with index 0
python simsearch.py ../data/aux_npfp_*date*.csv -q 0
You can also perform a similarity search for multiple queries by adding addtional indices.
python simsearch.py ../data/aux_npfp.csv -q 0 15 8 1 84
A new folder will be generated containing the results of the similairty search Like in the original paper, the cosine similarity is used for the search.
You can add the -d
to not include the other queries in the similarity search.