ORGANIC

ORGANIC is an efficient molecular discovery tool, able to create molecules with desired properties. It has a user-oriented interface, and doesn't require a HPC cluster. Feel free to check our article about ORGANIC, and/or contact the developers if you have any issue or are interested in collaborations.

This implementation is authored by Carlos Outeiral (carlos@outeiral.net), Benjamin Sanchez-Lengeling (beangoben@gmail.com), Gabriel Guimaraes (gabrielguimaraes@college.harvard.edu) and Alan Aspuru-Guzik (alan@aspuru.com), affiliated to Harvard University, Department of Chemistry and Chemical Biology, at the time of release.

Installation

How-to

To install, just clone our repo:

git clone https://github.com/couteiral/ORGANIC.git

And, it is done!

Requirements

• tensorflow==1.2
• future==0.16.0
• rdkit
• keras
• GPmol
• numpy
• scipy
• pandas
• tqdm
• pymatgen

How to use

ORGANIC has been carefully designed to be simple, while still allowing full customization of every parameter in the models. Have a look at a minimal example of our code:

model = ORGANIC('OPVs')                     # Loads a ORGANIC with name 'OPVs'
model.load_training_set('opv.smi')          # Loads the training set (molecules encoded as SMILES)
model.set_training_program(['PCE'], [50])   # Sets the training program as 50 epochs with the PCE metric
model.load_metrics()                        # Loads all the metrics
model.train()                               # Proceeds with the training

If you are interested, we encourage you to read our '10 minutes to ORGANIC' tutorial, where you can learn all (literally, all) the functionalities of ORGANIC.