The in silico prediction of partition coefficients is an important task in computer-aided drug discovery. In particular the octanol–water partition coefficient is used as surrogate for lipophilicity. Various computational approaches have been proposed, ranging from simple group-contribution techniques based on the 2D topology of a molecule to rigorous methods based molecular dynamics (MD) or quantum chemistry. In order to balance accuracy and computational cost, we recently developed the MD fingerprints (MDFPs), where the information in MD simulations is encoded in a floating-point vector, which can be used as input for machine learning (ML). The MDFP-ML approach was shown to perform similarly to rigorous methods while being substantially more efficient. Here, we present the application of MDFP-ML for the prediction of octanol–water partition coefficients in the SAMPL6 blind challenge. The underlying computational pipeline is made freely available in form of the MDFPtools package.

中文翻译：

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在SAMPL6辛醇-水log P盲挑战中使用分子动力学指纹（MDFP）。

计算机分配系数的计算机预测是计算机辅助药物发现中的重要任务。特别是，辛醇-水分配系数被用作亲脂性的替代物。已经提出了各种计算方法，从基于分子2D拓扑的简单基团贡献技术到基于分子动力学（MD）或量子化学的严格方法。为了平衡准确性和计算成本，我们最近开发了MD指纹（MDFP），其中MD仿真中的信息被编码在浮点向量中，该浮点向量可用作机器学习（ML）的输入。事实证明，MDFP-ML方法的执行方式与严格方法相似，但效率更高。这里，我们介绍了MDFP-ML在SAMPL6盲挑战中预测辛醇-水分配系数的应用。基础计算管道可以以MDFPtools软件包的形式免费使用。