Department of Computer Science, University of Chicago, Chicago, IL, USA.
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.
Methods Mol Biol. 2022;2390:301-319. doi: 10.1007/978-1-0716-1787-8_13.
Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.
超高通量虚拟筛选（uHTVS）是一个新兴领域，将经典对接技术与高通量 AI 方法联系在一起。我们概述了机械对接模型的目标和成功。我们提出了不同的 AI 加速 uHTVS 工作流程，主要是通过替代对接模型。我们展示了一种新的特征表示技术，分子描述（图像），作为对接的替代模型。在讨论了在数十亿规模上使用回归富集表面分析屏幕之后，我们概述了深度学习在 uHTVS 筛选管道中的未来。
