If you're familiar with docking workflows, this project could be a valuable tool for your research.
Everything should now be streamlined and ready to use.
To begin, download your desired ligand files using only one wget file -which has a lot of wget command- from any ligand databases, and place it in the "docking" folder of your working directory. Then, position your macromolecule in the MACRO_DIR folder. After that, simply run the run.py script inside the "docking" folder. The script will automatically download the ligands, split the molecules, create and center the AutoDock Vina simulation grid on the macromolecule, and initiate the simulation. This should meet most of your docking needs. I've drastically automated the entire pipeline, and my next goal is to integrate GPU acceleration—though I haven't found the time for that yet. And maybe adding a step-by-step guide would be nice. Be sure to configure the resource allocation in the scripts according to your system specifications for optimal performance. Give it a try!
Once the docking process is complete, the focus shifts to finding ligands with the best affinity—those that are in favorable geometric positions and exhibit low binding energy. To help with this, convert all .pdbqt files into readable data using the output-analyses.py script. I've opted to use CSV format due to the large amount of data, but feel free to choose a format that works best for you. The script utilizes Pandas to efficiently process and sort the data.
Since this process demands fast random read/write operations, using a traditional hard drive may result in significant delays. I recommend using a high-speed NVMe SSD, or even better, an Intel Optane drive. INTEL, ARE YOU LISTENING?
For context, I ran simulations on all ligands from the wget file, which took approximately 3 days. After processing, I generated over 1.2 million ligand files, taking up around 80GB of storage. My setup is modest—a Ryzen 5 3600X with 24GB of RAM. If you have access to a more powerful server with many cores and fast NVMe storage (Optane would be ideal), please reach out. I would love to run simulations for a wider range of molecules.
Currently, I've uploaded my results from docking with 4H10. I identified a few promising candidates, but keep in mind—I’m not a molecular physicist or bioinformatician, just a physicist exploring more advanced simulations.