A study on Generating Molecules with Variational Auto-encoders based on Graph Neural Networks

그래프 신경망 기반 가변 자동 인코더로 분자 생성에 관한 연구

Extracting informative representation of molecules using graph neural networks(GNNs) is crucial in AI-driven drug discovery. Recently, the graph research community has been trying to replicate the success of self supervised in natural language processing, with several successes claimed. However, we find the benefit brought by self-supervised learning on applying varitional auto-encoders can be potentially effective on molecular data.