Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, 02115.
Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts, 02115.
J Comput Chem. 2019 Mar 15;40(7):885-892. doi: 10.1002/jcc.25772. Epub 2019 Jan 7.
The conversion of polymer parameterization from internal coordinates (bond lengths, angles, and torsions) to Cartesian coordinates is a fundamental task in molecular modeling, often performed using the natural extension reference frame (NeRF) algorithm. NeRF can be parallelized to process multiple polymers simultaneously, but is not parallelizable along the length of a single polymer. A mathematically equivalent algorithm, pNeRF, has been derived that is parallelizable along a polymer's length. Empirical analysis demonstrates an order-of-magnitude speed up using modern GPUs and CPUs. In machine learning-based workflows, in which partial derivatives are backpropagated through NeRF equations and neural network primitives, switching to pNeRF can reduce the fractional computational cost of coordinate conversion from over two-thirds to around 10%. An optimized TensorFlow-based implementation of pNeRF is available on GitHub at https://github.com/aqlaboratory/pnerf © 2018 Wiley Periodicals, Inc.
聚合物参数化从内部坐标（键长、角度和扭转）转换为笛卡尔坐标是分子建模中的一项基本任务，通常使用自然延伸参考框架（NeRF）算法来完成。NeRF 可以并行化以同时处理多个聚合物，但不能沿着单个聚合物的长度进行并行化。已经推导出了一种在聚合物长度上可并行化的数学等效算法 pNeRF。经验分析表明，使用现代 GPU 和 CPU 可以实现数量级的加速。在基于机器学习的工作流程中，其中偏导数通过 NeRF 方程和神经网络基元反向传播，切换到 pNeRF 可以将坐标转换的计算成本从超过三分之二降低到大约 10%。pNeRF 的基于 TensorFlow 的优化实现可在 GitHub 上获得，网址为 https://github.com/aqlaboratory/pnerf © 2018 Wiley Periodicals, Inc.
