Graph

Construct (python library)

Politics

Chemical space

Data mining

Geodesy

Epistemology

Machine learning

Philosophy

Workflow

Political science

Benchmark (surveying)

Artificial intelligence

Property (philosophy)

Geography

Chemistry

Programming language

Representation (politics)

Molecular graph

Computer science

Drug discovery

Theoretical computer science

Artificial neural network

Biochemistry

Database

Convolutional neural network

Journal of Chemical Information and Modeling

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 a wide variety of chemical end points. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.

Analyzing Learned Molecular Representations for Property Prediction