Submitted to Nature Computational Science.
RRCGAN is a deep generative model using a Generative Adversarial Network (GAN) combined with a Regressor to generate molecules with targeted properties. It is puerly run in Python. Using GPU is necessary, otherwise running the code takes a lot!
RRCGAN is a generative GAN model designed to generate small molecules with targeted properties. RRCGAN, a generative deep learning model, has been built in Keras from Tensorflow that can easily installed on personal computers. Having a GPU is recommended to accelerate each epochs of learning. The packages used in RRCGAN can be installed on all major platforms (e.g. BSD, GNU/Linux, OS X, Windows).
RRCGAN requires only a standard computer with GPU and enough RAM.
RRCGAN mainly depends on the Python scientific stack, Keras form Tensorflow, and chemistry tools chainer chemistry and RDKit.
numpy
scipy
scikit-learn
pandas
seaborn
sklearn
tensorflow
matplotlib
chainer chemistry
RDKit
The only challenge for running the model is to set up the Tensorflow-gpu. One should install specific version of Tensorflow and Nvidia drivers to make it work. The necessary packages and the built conda environment used is mentioned in environment.yml. Installing the whole packages and running Tensorflow-gpu may take 30-60 mins.
We primarily used Lewis Cluster from University of Missouri-Columbia for running the code. The following is the information of a personal machine that was tested for running the tensorflow on GPU.
-GPU Nvidia RTX 2080 Super, Cuda version: 10.1, cuDNN: 7.6, Tensorflow: 2.11.0.
This project is covered under the Apache 2.0 License.