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.

我们提高了质量，并减少了生产用于小分子抗病毒设计的机器学习模型的时间。我们的全球异步多级并行培训方法可将Sierra的全部业务扩展到97.7％的效率。我们训练了一种新颖的，基于字符的Wasserstein自动编码器，该编码器在23分钟内对16.13亿种化合物进行了训练，从而产生了更高质量的模型，而先前的先进技术每天需要处理100万种化合物。将培训时间从一天缩短到几分钟，将模型创建的瓶颈从计算机工作周转时间转变为人工创新时间。我们的实现实现了318个PFLOP，占半精度峰值的17.1％。我们会将这个模型整合到分子设计循环中，以生成更多种不同的化合物。寻找小说，