Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models
| Autor: | Kevin McLoughlin, Felice C. Lightstone, David Hysom, Sam Ade Jacobs, Dong H. Ahn, Tim Moon, Ian Karlin, John Gyllenhaal, Jonathan E. Allen, Derek Jones, Pythagoras Watson, Brian Van Essen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0303 health sciences
2019-20 coronavirus outbreak Coronavirus disease 2019 (COVID-19) Computer science business.industry Distributed computing Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Deep learning 010402 general chemistry 01 natural sciences Small molecule 0104 chemical sciences Theoretical Computer Science 03 medical and health sciences Hardware and Architecture Asynchronous communication Scalability Artificial intelligence business Software Generative grammar 030304 developmental biology |
| Zdroj: | The International Journal of High Performance Computing Applications. 35:469-482 |
| ISSN: | 1741-2846 1094-3420 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|