MolGPT2.0

This repository contains code and datasets for the paper "MolGPT2.0: Multi-objective molecule generation via transformer encoder-decoder model and direct preference optimization."

---

data contains dataset(dockstring) used for training base models in the file lck_dockstring_data1.csv, while Preference data used for performing DPO is present in pickle file PreferenceData_affinity.pkl .

---

src contains the code to train the base model, perform DPO, and analyse results.

---

How To Run The Code

1. Install required libraries using the requirements.txt. For dockstring, we recommend using conda from conda-forge, as this also installs correct version of openbabel which cannot be installed by pip currently.
   Use following commands for installing dockstring and other required packages

    conda install -c conda-forge dockstring
   
    pip install -r requirements.txt
   

2. For training base model, run following command in src directory (properties argument determines on which properties model is conditioned, and temp determines temp used for sampling molecules ) :

    python train_models.py --properties affinity --temp 1.0
   

This command trains an encoder - decoder model trained using conditioning properties, and samples molecules for some target values for that properties.

Base model and sampled molecules will be stored in checkpoints directory , inside the directory encoder_decoder_8_layer_affinity.

For multiple property conditioning, add desired properties after --properties. eg. python train_models.py --properties affinity logps sas tpsas qeds

3. For performing DPO on base model, use following command :

    python dpo_training.py --model_properties affinity --base_model_dir encoder_decoder_8_layer_affinity --output_dir dpo_encoder_decoder_8_layer_affinity
   

Here, the arguments are : --model_properties : properties on which base model is conditioned --base_model_dir : directory in which base model and results are stored --output_dir : directory in which to store results for dpo model

4. For computing properties of generated molecules and generating results and plots for them, use the command :

    python analysis_and_plots.py --checkpoint_dir encoder_decoder_8_layer_affinity_logps --properties affinity logps --plot_targets affinity=9.0,6.0 logps=1.0,3.0
   

Here, the arguments are : --checkpoint_dir : directory containing sampled molecules --properties : properties on which sampled molecules were conditioned --plot_targets : target values of property for which to plot kdes