PotentialNet for Molecular Property Prediction

The arc of drug discovery entails
a multiparameter optimization
problem spanning vast length scales. The key parameters range from
solubility (angstroms) to protein–ligand binding (nanometers)
to in vivo toxicity (meters). Through feature learninginstead
of feature engineeringdeep neural networks promise to outperform
both traditional physics-based and knowledge-based machine learning
models for predicting molecular properties pertinent to drug discovery.
To this end, we present the PotentialNet family of graph convolutions.
These models are specifically designed for and achieve state-of-the-art
performance for protein–ligand binding affinity. We further
validate these deep neural networks by setting new standards of performance
in several ligand-based tasks. In parallel, we introduce a new metric,
the Regression Enrichment Factor EFχ(R), to measure the early enrichment of
computational models for chemical data. Finally, we introduce a cross-validation
strategy based on structural homology clustering that can more accurately
measure model generalizability, which crucially distinguishes the
aims of machine learning for drug discovery from standard machine
learning tasks.