You will find herein the code and files related to our paper:
The example directory contains Jupyter Notebook which the users can use to predict the class for their molecules against YTHDF1
To run the notebook, users have to install the libraries using the requirments.yml file and should activate the environment
conda env create -f requirments.yml
conda activate SBVS-YTHDF1
Muhammad Junaid, Bo Wang and Wenjin Li. Data augmentation helps to improve the performance of the scoring function in structured-based virtual screening for m6A reader protein YTHDF1
We advise you to refer to our following Nature Protocols paper to better understand SBVS
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.