Awesome
moo-denovo
GUI of the method described in: "De novo drug design of targeted chemical libraries based on artificial intelligence and multi-objective optimization" (submitted). The code is build based on https://github.com/MarcusOlivecrona/REINVENT
Requirements and Installation
This package requires Anaconda Python 3.6 and the following packages:
- pyqt
- matplotlib
- pytorch
- rdkit
- pexpect
- tensorflow
- molsets (pip package)
Installation:
conda create --name moo-denovo python=3.6
conda activate moo-denovo
conda install -c conda-forge rdkit
conda install pyqt matplotlib pytorch pexpect tensorflow
pip install molsets
Usage
Run the application with
python app.py
Otimization
In the optimize tab you can select the number and the descriptors to optimize setting the miniumum and maximum value. Opt field can be used, selecting the Similarity and UserFragment descriptor, in order to specify the SMILES of the reference molecule or fragment respectively.
Generation
In the generate tab you can use the optimized model to generate a molecular library. First select the desired .ckpt file of the generative model obtained in the Optimization stage.