Innovative Drug-like Molecule Generation from Flow-based Generative Model
This work aims to improve drug design for pharmaceutical applications by generating more realistic molecules, but it appears incremental as it builds directly on GraphBP.
The paper tackles the problem of generating drug-like molecules for protein targets by addressing the limitation of existing methods that treat proteins as rigid bodies, proposing an extension of GraphBP that incorporates protein dynamics from the Protein Data Bank. Results will be evaluated using validity and binding affinity metrics through computational chemistry algorithms.
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.