The linear interaction energy (LIE) approach is an end–point method to compute binding affinities. As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy ΔG_bind. This perspective summarizes our recent efforts to use molecular simulation and empirically calibrated LIE models for accurate and efficient calculation of ΔG_bind for diverse sets of compounds binding to flexible proteins (e.g., Cytochrome P450s and other proteins of direct pharmaceutical or biochemical interest). Such proteins pose challenges on ΔG_bind computation, which we tackle using a previously introduced statistically weighted LIE scheme. Because calibrated LIE models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space. To enable AD assessment of LIE predictions (or other protein-structure and -dynamic based ΔG_bind calculations) we recently introduced strategies for AD assignment of LIE models, based on simulation and training data only. These strategies are reviewed here as well, together with available tools to facilitate and/or automate LIE computation (including software for combined statistically-weighted LIE calculations and AD assessment).

线性相互作用能（LIE）方法是一种计算结合亲和力的终点方法。因此，它结合了显式的构象采样（蛋白质结合和未结合的配体状态）与计算蛋白质-配体结合自由能Δ的效率G_捆绑。这个观点总结了我们最近使用分子模拟和经验校准的LIE模型进行Δ的准确高效计算的努力。G_捆绑用于与柔性蛋白结合的各种化合物（例如，细胞色素P450和其他具有直接药学或生化意义的蛋白）。这些蛋白质对Δ构成挑战G_捆绑计算，我们使用先前介绍的统计加权LIE方案解决。由于校准的LIE模型需要对比例参数进行经验拟合，因此它们需要伴随适用性域（AD）定义，以为由集体化学和相互作用空间定义的参考框架内的任意查询化合物的预测提供置信度。为了能够进行LIE预测的AD评估（或其他基于蛋白质结构和-dynamic的ΔG_捆绑计算），我们最近仅基于模拟和训练数据引入了LIE模型的AD分配策略。这些策略以及可促进和/或自动进行LIE计算的可用工具（包括用于组合统计加权LIE计算和AD评估的软件）也将在此处进行回顾。