The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating the entire process. No single methodological approach can achieve the necessary accuracy with required efficiency. Here we describe multiple algorithmic innovations to overcome this fundamental limitation, development and deployment of computational infrastructure at scale integrates multiple artificial intelligence and simulation-based approaches. Three measures of performance are:(i) throughput, the number of ligands per unit time; (ii) scientific performance, the number of effective ligands sampled per unit time and (iii) peak performance, in flop/s. The capabilities outlined here have been used in production for several months as the workhorse of the computational infrastructure to support the capabilities of the US-DOE National Virtual Biotechnology Laboratory in combination with resources from the EU Centre of Excellence in Computational Biomedicine.

中文翻译：

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无可挑剔：通过评估更好的 LEads 实现 COVID 治愈的集成建模管道

目前制药行业采用的药物发现过程通常需要大约 10 年和 2-30 亿美元才能交付一种新药。这既太贵又太慢，尤其是在 COVID-19 大流行等紧急情况下。In silicomethodologies 需要改进以更好地选择先导化合物，这些化合物可以进入药物发现协议的后期阶段，加速整个过程。没有单一的方法论能够以所需的效率实现必要的准确性。在这里，我们描述了多种算法创新来克服这一基本限制，大规模计算基础设施的开发和部署集成了多种人工智能和基于模拟的方法。三个性能指标是：(i) 吞吐量，单位时间内配体的数量；(ii) 科学性能，每单位时间采样的有效配体的数量和 (iii) 峰值性能，以 flop/s 为单位。此处概述的功能已作为计算基础设施的主力在生产中使用了几个月，以支持美国能源部国家虚拟生物技术实验室与欧盟计算生物医学卓越中心的资源相结合的能力。