Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration,Journal of Computational Chemistry

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Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
Journal of Computational Chemistry ( IF 3.4 ) Pub Date : 2018-09-30 , DOI: 10.1002/jcc.25520
Wei Chen ₁ , Andrew L. Ferguson _{1,

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Affiliation

Macromolecular and biomolecular folding landscapes typically contain high free energy barriers that impede efficient sampling of configurational space by standard molecular dynamics simulation. Biased sampling can artificially drive the simulation along prespecified collective variables (CVs), but success depends critically on the availability of good CVs associated with the important collective dynamical motions. Nonlinear machine learning techniques can identify such CVs but typically do not furnish an explicit relationship with the atomic coordinates necessary to perform biased sampling. In this work, we employ auto‐associative artificial neural networks (“autoencoders”) to learn nonlinear CVs that are explicit and differentiable functions of the atomic coordinates. Our approach offers substantial speedups in exploration of configurational space, and is distinguished from existing approaches by its capacity to simultaneously discover and directly accelerate along data‐driven CVs. We demonstrate the approach in simulations of alanine dipeptide and Trp‐cage, and have developed an open‐source and freely available implementation within OpenMM. © 2018 Wiley Periodicals, Inc.

中文翻译：

使用自动编码器进行分子增强采样：实时集体变量发现和加速自由能景观探索

大分子和生物分子折叠景观通常包含高自由能障碍，阻碍了通过标准分子动力学模拟对构型空间进行有效采样。偏置采样可以沿着预先指定的集体变量 (CV) 人为地驱动模拟，但成功关键取决于与重要集体动力学运动相关的良好 CV 的可用性。非线性机器学习技术可以识别此类 CV，但通常不提供与执行偏置采样所需的原子坐标的明确关系。在这项工作中，我们使用自关联人工神经网络（“自编码器”）来学习非线性 CV，它是原子坐标的显式和可微函数。我们的方法在探索配置空间方面提供了显着的加速，并且与现有方法的区别在于它能够同时发现和直接沿着数据驱动的 CV 加速。我们在丙氨酸二肽和 Trp 笼的模拟中演示了该方法，并在 OpenMM 中开发了一个开源且免费可用的实现。© 2018 Wiley Periodicals, Inc.

更新日期：2018-09-30

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