  Is it feasible to create an analysis paradigm that can analyze and then
accurately and quickly predict known drugs from experimental data? PharML.Bind
is a machine learning toolkit which is able to accomplish this feat. Utilizing
deep neural networks and big data, PharML.Bind correlates
experimentally-derived drug affinities and protein-ligand X-ray structures to
create novel predictions. The utility of PharML.Bind is in its application as a
rapid, accurate, and robust prediction platform for discovery and personalized
medicine. This paper demonstrates that graph neural networks (GNNs) can be
trained to screen hundreds of thousands of compounds against thousands of
targets in minutes, a vastly shorter time than previous approaches. This
manuscript presents results from training and testing using the entirety of
BindingDB after cleaning; this includes a test set with 19,708 X-ray structures
and 247,633 drugs, leading to 2,708,151 unique protein-ligand pairings.
PharML.Bind achieves a prodigious 98.3% accuracy on this test set in under 25
minutes. PharML.Bind is premised on the following key principles: 1) speed and
a high enrichment factor per unit compute time, provided by high-quality
training data combined with a novel GNN architecture and use of
high-performance computing resources, 2) the ability to generalize to proteins
and drugs outside of the training set, including those with unknown active
sites, through the use of an active-site-agnostic GNN mapping, and 3) the
ability to be easily integrated as a component of increasingly-complex
prediction and analysis pipelines. PharML.Bind represents a timely and
practical approach to leverage the power of machine learning to efficiently
analyze and predict drug action on any practical scale and will provide utility
in a variety of discovery and medical applications.
