Neural Fingerprint-based Molecular Docking

A repository enabling a deep-learning approach to molecular docking. Utilizes differentiable, trainable, neural fingerprints to filter dynamic-sized databases of molecules based on binding favorability to proteins/binding sites. Uses activations to isolate most significant portions of graph.

steps to run:

make sure you have all the necessary python libraries installed with pip install -r requirements.txt

1. use python createModels.py ... to populate the src folder with training scripts

• python createModels.py -f/--fpl <fingerprint length> -p/--protein <protein name>
• can alter hyperparameters in createModels.py if you'd like; to alter architecture, make sure you change the architecture in the base model in createModels.py and train.py
  
  

2. use sbatch trainModels.sh if using slurm

• fill in slurm configuration according to resources available, etc.
• on the last line in trainModels.sh, make sure to fill in the path to the log file as well as path to the scripts. Use $SLURM_ARRAY_TASK_ID to vary names of log and model based on slurm array
  
  

3. results

• results will be populated in src/hpResults.csv. Files with more data are named corresponding to the model number and are withinn src/trainingJobs/* with various names
• logs for runs are in src/logs

Acknowledgements to Xuhan Liu and the work of Gentile et. al. for some of the code in this repository