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submitted on 18.09.2019 and posted on 23.09.2019by Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen
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