Relative binding affinity prediction is a critical component in computer aided drug design. A significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of being random in an objective manner. Although many performance metrics, such as the Pearson correlation coefficient (r), mean unsigned error (MUE), and root-mean-square error (RMSE), are frequently used in the literature, a true and nontrivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely, the prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are (1) it provides the uncertainty range in the predicted activities, which is important in prospective applications, and (2) a true null model with well-defined PI can be established. We provide one such example termed the Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, σ). Having an analytically defined PI that only depends on the variation in the activities, GRAM should, in principle, allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.

中文翻译：

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GRAM：相对绑定亲和力预测的True Null模型。

相对结合亲和力预测是计算机辅助药物设计中的关键组成部分。致力于开发快速可靠的计算机方法的大量工作。但是，对它们的性能进行可靠的评估仍然是一个复杂的问题，因为它需要适用于预期环境的性能度量，更重要的是需要一个真正的空模型，该模型以客观的方式定义随机预期的性能。尽管在文献中经常使用许多性能指标，例如Pearson相关系数（r），平均无符号误差（MUE）和均方根误差（RMSE），但尚未确定出真实且不重要的空模型。为了解决这个问题，在这里我们引入间隔估计作为一种额外的度量，即预测间隔（PI），可以从预测的误差分布中进行估算。使用间隔估计的好处是：（1）它提供了预测活动的不确定性范围，这在预期应用中很重要；（2）可以建立具有明确定义的PI的真实零模型。我们提供了一个称为高斯随机亲和力模型（GRAM）的示例，该示例基于经验观察，即典型线索优化工作中的亲和力变化具有正常分布N（0，σ）的趋势。从理论上来说，通过定义分析型PI仅取决于活动的变化，GRAM应该可以使我们以标准方式比较相对结合亲和力预测方法的性能，最终对于衡量算法开发的进展至关重要。