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 <i>a priori</i> 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.
Learning in Abstract Memory Schemes for Dynamic Optimization
