A graph representation of molecular ensembles for polymer property prediction.

ABSTRACT: Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties. However, in contrast to organic molecules, polymers are often not well-defined single structures but an ensemble of similar molecules, which poses unique challenges to traditional chemical representations and machine learning approaches. Here, we introduce a graph representation of molecular ensembles and an associated graph neural network architecture that is tailored to polymer property prediction. We demonstrate that this approach captures critical features of polymeric mat

SUBMITTER: Aldeghi M 

PROVIDER: S-EPMC9473492 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature