Analyzing Learned Molecular Representations for Property Prediction.

ABSTRACT: Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial data sets spanning

SUBMITTER: Yang K 

PROVIDER: S-EPMC6727618 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature