KekuleScope

This repository contains the 33 data sets, code and scripts needed to generate the compound activity prediction models reported in:

KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images

Isidro Cortés-Ciriano and Andreas Bender

Journal of Cheminformatics, 2019, 11:41

Dependencies

KekuleScope depends on the following python libraries:

scipy==1.0.0
numpy==1.15.1
joblib==0.11
ipython==7.1.1
Pillow==5.3.0
profilehooks==1.10.0
rdkit==2009.Q1-1
scikit_learn==0.20.0
torch==0.4.1.post2
torchvision==0.2.1

To install these libraries run:

pip install -r requirements.txt

Running the code

To obtain help on how to run the models run:

python Kekulescope.py --help

It is assumed that the user has access to at least 1 GPU card.

Contact

Please contact Isidro Cortés Ciriano, PhD (isidrolauscher at gmail.com) for further information or suggestions.

Funding

This Project has received funding from the European Union’s Framework Programme For Research and Innovation Horizon 2020 (2014–2020) under the Marie Curie Sklodowska-Curie Grant Agreement No. 703543 (I.C.C.).