RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design

Understand

De novo molecule generation often results in chemically unfeasible molecules.

• A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility.
• However, using currently available proxies still results in highly unrealistic compounds.
• We investigate the feasibility of training deep graph neural networks to approximate the outputs of a retrosynthesis planning software, and their use to bias the search process.