Stochastic-based Generative Network Complex

A python package for the Stochastic-based Generative Network Complex

1 Preperation for Predictive Models

1.1 Training datasets

1.2 Train predictive model

2 Preperation for Reference and Init Vector

2.1 Reference

2.2 Init vector

3 Generator Model

1. Submit thousands of jobs to generate latent space vectors.

   cd sbatch
   python submit_generator.py 20221209

2. Divide generated latent space vectors to sub-files. Each sub-file has 2000 records.

   cd ..
   python ./utils/divide_generated_ls.py 20221209

3. Decode all generated latent space vectors to smiles.

   cd sbatch
   python submit_decode.py 20221209

4. Drop duplicated and unlikely smiles.

   cd ..
   python ./utils/drop_duplicates.py 20221209

4 Filtered Model

1. Encode generated smiles to latent space vectors

   cd sbatch
   python submit_encoder.py 20221209

2. Binding affinity test

   cd ..
   python ./src/filtered.py --date 20221209

3. ADMET and SAS test

   Test ADMET on a online server: ADMET and download a csv file. Then transfer this file to server

   scp ADMET.csv wangru25@hpcc.msu.edu:/mnt/research/guowei-search.8/RuiWang/FokkerPlanckAutoEncoder/results/generator_20221209

   Then check if there is a molecule that falls in the optimal range.

   python ./src/properties.py 20221209

5 Check the Reproduction Rate

1. Decode latent space vectors (from encoder) to smiles.
2. Make comparasion with the generated smiles. Check the reproduction rate.

Reference

[1] Wang, R., Feng, H. and Wei, G.W., 2023. ChatGPT in Drug Discovery: A Case Study on Anticocaine Addiction Drug Development with Chatbots. Journal of Chemical Information and Modeling, 63(22), pp.7189-7209.