You will find herein the code and files related to our paper:
Caba, K., Tran-Nguyen, V. K., Rahman, T. & Ballester, P. J. Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors. J Cheminform 16, 40 (2024)
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00832-1
All output and input data related to this paper can be found in the "data" folder.
The code (Jupyter notebooks) can be found in the "code" folder. We advise you to refer to our following Nature Protocols paper to better understand these notebooks:
Tran-Nguyen, V. K., Junaid, M., Simeon, S. & Ballester, P. J. A practical guide to machine-learning scoring for structure-based virtual screening. Nat. Protoc. (2023).
The protocol-env environment has to be set up before the notebooks are used: please find the protocol-env.yml file for this purpose in our MLSF-protocol github repository: https://github.com/vktrannguyen/MLSF-protocol.