Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester M1 7DN, Great Britain.
J Phys Chem A. 2010 Mar 18;114(10):3371-83. doi: 10.1021/jp9105585.
Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex that it is impractical or impossible to model them by ab initio methods. For this reason there is a need for accurate potentials that are able to quickly reproduce ab initio quality results at the fraction of the cost. The interactions within force fields are represented by a number of functions. Some interactions are well understood and can be represented by simple mathematical functions while others are not so well understood and their functional form is represented in a simplistic manner or not even known. In the last 20 years there have been the first examples of a new design ethic, where novel and contemporary methods using machine learning, in particular, artificial neural networks, have been used to find the nature of the underlying functions of a force field. Here we appraise what has been achieved over this time and what requires further improvements, while offering some insight and guidance for the development of future force fields.
分子力学是用于对太大或太复杂而无法通过从头计算方法进行建模的系统进行建模的首选工具。出于这个原因，需要能够以成本的一小部分快速再现从头计算质量结果的准确势能。力场中的相互作用由许多函数表示。一些相互作用是众所周知的，可以用简单的数学函数来表示，而另一些相互作用则不是那么容易理解，它们的函数形式以简化的方式表示，甚至尚不清楚。在过去的 20 年中，已经出现了一种新的设计理念的第一个例子，即使用机器学习，特别是人工神经网络，使用新颖和现代的方法来寻找力场基础函数的本质。在这里，我们评估了在这段时间内取得的成就以及需要进一步改进的地方，同时为未来力场的发展提供了一些见解和指导。
