Implementation of the Paper "Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations" by Robin Winter, Floriane Montanari, Frank Noe and Djork-Arne Clevert.¹

python 3.6
tensorflow 1.10
numpy<=1.14.5
pandas<=1.0.3
rdkit
scikit-learn

Create a new enviorment with above mentioned prerequisites and:

pip install cddd  # with GPU support

or

pip install cddd-cpu  # without GPU support

Or simply utilize the repspective .yml file:

conda env create -f environment_gpu.yml  # with GPU support

or

conda env create -f environment_cpu.yml  # without GPU support

A pretrained model as described in ref. 1 is available on Google Drive. It is automatically downloaded to the default user data dir (as defined by appdirs). It can also wbe download and unzip by execuiting the bash script "download_default_model.sh":

./download_default_model.sh

The default_model.zip file can also be downloaded manualy under https://drive.google.com/open?id=1oyknOulq_j0w9kzOKKIHdTLo5HphT99h

Extract molecular descriptors from two QSAR datasets (ref. 2,3) and evaluate the perfromance of a SVM trained on these descriptors.

cd example
python run_qsar_test.py

or with gpu support on e.g. device 0:

python run_qsar_test.py --use_gpu --device 0

The accuracy on the Ames dataset should be arround 0.814 +/- 0.006.

The r2 on the Lipophilicity dataset should be arround 0.731 +/- 0.029.

Run the script run_cddd.py to extract molecular descripotrs of your provided SMILES:

cddd --input smiles.smi --output descriptors.csv  --smiles_header smiles

Supported input:

• .csv-file with one SMILES per row
• .smi-file with one SMILES per row

For .csv: Specify the header of the SMILES column with the flag --smiles_header (default: smiles)

The pretrained model can also be imported and used directly in python via the inference class:

import pandas as pd
from cddd.inference import InferenceModel
from cddd.preprocessing import preprocess_smiles

Load and preprocess data:

ames_df = pd.read_csv("example/ames.csv", index_col=0)
ames_df["smiles_preprocessed"] = ames_df.smiles.map(preprocess_smiles)
ames_df = ames_df.dropna()
smiles_list = ames_df["smiles_preprocessed"].tolist()

Create a instance of the inference class:

inference_model = InferenceModel()

Encode all SMILES into the continuous embedding (molecular descriptor):

smiles_embedding = inference_model.seq_to_emb(smiles_list)

The infernce model instance can also be used to decode a molecule embedding back to a interpretable SMILES string:

decoded_smiles_list = inference_model.emb_to_seq(smiles_embedding)

[1] R. Winter, F. Montanari, F. Noe and D. Clevert, Chem. Sci, 2019, https://pubs.rsc.org/en/content/articlelanding/2019/sc/c8sc04175j#!divAbstract

[2] K. Hansen, S. Mika, T. Schroeter, A. Sutter, A. Ter Laak, T. Steger-Hartmann, N. Heinrich and K.-R. MuÌ´Lller, J. Chem. Inf. Model., 2009, 49, 2077–2081.

[3] Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing and V. Pande, Chemical Science, 2018, 9, 513–530.