Enabling Rapid COVID-19 Small Molecule Drug Design Through Scalable Deep Learning of Generative Models

Sam Ade Jacobs, Brian Van Essen, Felice C Lightstone

Abstract:

We improved the quality and reduced the time to produce machine learned models for use in small molecule antiviral design. Our globally asynchronous multi-level parallel training approach strong scales to all of Sierra with up to 97.7\% efficiency. We trained a novel, character-based Wasserstein autoencoder that produces a higher quality model trained on 1.613 billion compounds in 23 minutes while the previous state of the art takes a day on 1 million compounds. Reducing training time from a day to minutes shifts the model creation bottleneck
from computer job turnaround time to human innovation time. Our implementation achieves 318 PFLOPs for 17.1\% of half-precision peak.
We will incorporate this model into our molecular design loop enabling the generation of more diverse compounds; searching for novel, candidate antiviral drugs improves and reduces the time to synthesize compounds to be tested in the lab.