  To design a drug given a biological molecule by using deep learning methods,
there are many successful models published recently. People commonly used
generative models to design new molecules given certain protein. LiGAN was
regarded as the baseline of deep learning model which was developed on
convolutional neural networks. Recently, GraphBP showed its ability to predict
innovative "real" chemicals that the binding affinity outperformed with
traditional molecular docking methods by using a flow-based generative model
with a graph neural network and multilayer perception. However, all those
methods regarded proteins as rigid bodies and only include a very small part of
proteins related to binding. However, the dynamics of proteins are essential
for drug binding. Based on GraphBP, we proposed to generate more solid work
derived from protein data bank. The results will be evaluated by validity and
binding affinity by using a computational chemistry algorithm.
