Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

23 September 2019, Version 1
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.


We introduce Attention and Edge Memory schemes to the existing Message Passing Neural Network framework for graph convolution, and benchmark our approaches against eight different physical-chemical and bioactivity datasets from the literature. We remove the need to introduce a priori knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.


Graph convolution
message passing neural network
virtual screening
machine Learning
Deep learning neural network

Supplementary materials

Supplemental Information


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