The partition coefficient between octanol and water (logP) has been an important descriptor in QSAR predictions for many years and therefore the prediction of logP has been examined countless times. One of the best performing models is to predict the logP using multiple methods and average the result. We have used those averaged predictions to develop a training-set which was able to distil the information present across the disparate logP methods into one single model. Our model was built using extendable atom-types, where each atom is distilled down into a 6 digit number, and each individual atom is assumed to have a small additive effect on the overall logP of the molecule. Beyond the simple coefficient model a consensus model is evaluated, which uses known compounds as a starting point in the calculation and modifies the experimental logP using the same coefficients as in the first model. We then test the performance of our models against two different datasets, one where many different models routinely perform well against, and another designed to more represent pharmaceutical space. The true strength of the model is represented in the pharmaceutical benchmark set, where both models perform better than any previously developed models.

中文翻译：

---

JPlogP：使用预测数据训练的改进的 logP 预测器。

多年来，辛醇和水之间的分配系数 (logP) 一直是 QSAR 预测中的重要描述符，因此 logP 的预测已被检验了无数次。性能最好的模型之一是使用多种方法预测 logP 并对结果进行平均。我们使用这些平均预测来开发一个训练集，该训练集能够将不同 logP 方法中存在的信息提取到一个模型中。我们的模型是使用可扩展的原子类型构建的，其中每个原子都被分解为 6 ​​位数字，并且假设每个单独的原子对分子的整体 logP 具有很小的加性效应。除了简单系数模型之外，还评估了共识模型，该模型使用已知化合物作为计算的起点，并使用与第一个模型中相同的系数修改实验 logP。然后，我们针对两个不同的数据集测试模型的性能，其中一个数据集有许多不同的模型通常表现良好，另一个数据集旨在更能代表制药领域。该模型的真正优势体现在制药基准集中，这两个模型的表现都比任何以前开发的模型都要好。