A deep learning approach for the blind logP prediction in SAMPL6 challenge

Prasad Samarjeet; Brooks Bernard R.

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A deep learning approach for the blind logP prediction in SAMPL6 challenge

机译：SAMPL6 挑战中盲目 logP 预测的深度学习方法

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titleAbstract/titlepWater 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./p

机译：摘要水辛醇分配系数是衡量分子亲脂性的指标，在药物发现领域具有重要意义。利用分子指纹图谱和深度神经网络，开发了一种计算预测分区系数对数（logP）的新方法。机器学习模型在包含 12,000 个分子的数据集上进行训练，并在 2000 个分子上进行了测试。在本文中，我们介绍了 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 份作品中排名前四分之一。结果表明，深度学习模型可以作为一种快速、准确和鲁棒的小分子logP高通量预测方法。

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来源
《Journal of Computer-Aided Molecular Design》 |2020年第5期|535-542|共8页
作者
Prasad Samarjeet; Brooks Bernard R.;
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作者单位

Laboratory of Computational Biology, National Heart, Lung and Blood Institute;

Biophysics and Biophysical Chemistry;

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原文格式 PDF
正文语种英语
中图分类电子计算机在化学中的应用;结构化学;
关键词
SAMPL6; LogP; Deep learning; Fingerprinting;

机译：SAMPL6;LogP;深度学习;指纹;

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A deep learning approach for the blind logP prediction in SAMPL6 challenge

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