Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)
Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)
复制标题
利用可解释人工智能（explable Artificial Intelligence， XAI）解释深度神经网络获得的丙氨酸二肽异构化反应坐标
DOI：
10.1063/5.0087310
复制
发表时间：
2022-04-21
影响因子：
4.4
通讯作者：
Matubayasi, Nobuyuki
中科院分区：
文献类型：
--
作者：
Kikutsuji, Takuma;Mori, Yusuke;Matubayasi, Nobuyuki
需要一种获得适当反应坐标的方法来识别复杂分子系统中区分产物和反应物的过渡态。最近，大量研究致力于使用深度学习文献中的人工神经网络获取反应坐标，其中许多集体变量通常在输入层中使用。然而，由于深度神经网络中非线性函数的复杂性，很难解释哪些集体变量对预测反应坐标有贡献的细节。为了克服这一限制，我们使用了局部可解释模型不可知解释 (LIME) 的可解释人工智能 (XAI) 方法和基于博弈论的框架，称为 Shapley 加法解释 (SHAP)。我们证明，XAI 使我们能够获得每个集体变量对反应坐标的贡献程度，该贡献程度是通过对真空中丙氨酸二肽异构化的提交者进行深度学习的非线性回归确定的。特别是，LIME 和 SHAP 都为预测的反应坐标提供了重要的特征，其特征是适当的二面角，与之前从提交者测试分析中报告的一致。本研究提供了一个人工智能辅助框架来解释适当的反应坐标，当自由度数量增加时，该框架具有相当大的意义。 (C) 2022 作者。
A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing the product and reactant in complex molecular systems. Recently, abundant research has been devoted to obtaining reaction coordinates using artificial neural networks from deep learning literature, where many collective variables are typically utilized in the input layer. However, it is difficult to explain the details of which collective variables contribute to the predicted reaction coordinates owing to the complexity of the nonlinear functions in deep neural networks. To overcome this limitation, we used Explainable Artificial Intelligence (XAI) methods of the Local Interpretable Model-agnostic Explanation (LIME) and the game theory-based framework known as Shapley Additive exPlanations (SHAP). We demonstrated that XAI enables us to obtain the degree of contribution of each collective variable to reaction coordinates that is determined by nonlinear regressions with deep learning for the committor of the alanine dipeptide isomerization in vacuum. In particular, both LIME and SHAP provide important features to the predicted reaction coordinates, which are characterized by appropriate dihedral angles consistent with those previously reported from the committor test analysis. The present study offers an AI-aided framework to explain the appropriate reaction coordinates, which acquires considerable significance when the number of degrees of freedom increases. (C) 2022 Author(s).