Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed using molecular fingerprints and a deep neural network. The machine learning model was trained on a dataset of 12,000 molecules and tested on 2000 molecules. In this article, we present our results for the blind prediction of logP for the SAMPL6 challenge. While the best submission achieved a RMSE of 0.41 logP units, our submission had a RMSE of 0.61 logP units. Overall, we ranked in the top quarter out of the 92 submissions that were made. Our results show that the deep learning model can be used as a fast, accurate and robust method for high throughput prediction of logP of small molecules.

中文翻译：

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SAMPL6 挑战中盲 logP 预测的深度学习方法。

水辛醇分配系数可作为分子亲脂性的量度，在药物发现领域很重要。使用分子指纹和深度神经网络开发了一种计算预测分配系数对数（logP）的新方法。机器学习模型在 12,000 个分子的数据集上进行训练，并在 2000 个分子上进行测试。在本文中，我们展示了 SAMPL6 挑战的 logP 盲预测结果。虽然最佳提交的 RMSE 为 0.41 logP 单位，但我们提交的 RMSE 为 0.61 logP 单位。总体而言，我们在提交的 92 份申请中名列前四分之一。我们的结果表明，深度学习模型可以作为一种快速、准确和稳健的方法来高通量预测小分子的 logP。