Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models. (September 2021) - Jacobs, Sam Ade; Moon, Tim; McLoughlin, Kevin; Jones, Derek; Hysom, David; Ahn, Dong H; Gyllenhaal, John; Watson, Pythagoras; Lightstone, Felice C; Allen, Jonathan E; Karlin, Ian; Van Essen, Brian
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.