Ligand - Protein

A repo for experimenting on creating ligands w/ high binding affinity to a target protein using ML.

Architecture consits of GAN and Transformer model. Smiles data was used for ligands and sequences for proteins.

Databases:

1. BindingDB - bindingdb.org - Discription
2. ATOM3D - zenodo.org
3. UniProt - uniprot.org

Organization

You can execute a run via ./run_experiment.py and see metrics and results via Tensorboard by running:

1. Model will be required and path to dataset if you haven't stored them in a sql table from a previous experiment with this model: --model [model name] --data_path [dataset path]
   
2. If it's your first experiment w/ this model you can save the given dataset in a table so they won't need to be processed again for another experiment by additionally writing -sql [-ho HOST] [-po PORT] [-db DATABASE] in command line.
3. If you trained that model already several times you can specify a checkpoint by giving the absolute path [--ckpt_path CKPT_PATH] . As default it will choose the best checkpoint - meaning the one with the least amount of loss.
4. If you want to use a saved dataset in a table enter -sql without specifying the sql connection info.
5. After a run write in command line: tensorboard --logdir [path to runs folder] and open the link provided to see results.

Your SQL credentials will be asked when executing an experiment:

USER:
PASSWORD:

Goals

Create a proper structure for my own DL project.