Screening the sparse library with AdaptiveFlow
First, we should download the tutorial files:
wget https://adaptiveflow.org/sites/adaptiveflow.org/files/tutorials/AFVS_GK.tar
Decompress the archive by using:
tar -xvf AFVS_GK.tar
From the resulting folder, copy the following files to the AFVS folder:
cp -v -r ./AFVS_GK/input-files/qvina02_rigid_receptor1/ AFVS_GK/input-files/receptor/ ~/AFVS/input-files/
Upload the Enamine_REAL_Space_2022q12_sparse.todo file, containing the sparse version of the VF library:\
Next, we should copy the Enamine_REAL_Space_2022q12_sparse.todo file to the AFVS folder, overwriting the todo.all file, using a command similar to the one below:
cp -v ~/Enamine_REAL_Space_2022q12_sparse.todo ./tools/templates/todo.all\
Next upload the all.ctrl file: \
and copy it to overwrite the file in the AFVS folder, using a command similar to the one below:
cp -v ~/all.ctrl ./tools/templates/all.ctrl
We are now ready to perform our screening campaign!
Return to /tools folder and run:
./afvs_prepare_folders.py (with --overwrite if you retry)
followed by:
./afvs_prepare_workunits.py
Depending on the number of generated workunits, adjust the numerical values in the following command:
./afvs_submit_jobs.py 1 2
Congratulations, your screen should now be running! You can monitor the status using 2 separate slurm commands, sbatch and squeue.
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