Time: 2020-4-29 17:00
Venue: Online Talk
Speaker: Chence Shi, Turing Class (Class of 2016)
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this talk, we will first give a brief introduction to computer-aided drug discovery. We will then present a novel template-free approach for retrosynthesis prediction called G2Gs. G2Gs formulates the problem as a graph to graphs translation task and transforms a target molecular graph into a set of reactant molecular graphs. G2Gs achieves the state-of-the-art performance and does not require any domain knowledge.