Methodology for hyperparameter tuning of deep neural networks for efficient and accurate molecular property prediction

IF 3.9 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xuan Dung James Nguyen, Y.A. Liu
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引用次数: 0

Abstract

This paper presents a methodology of hyperparameter optimization (HPO) of deep neural networks for molecular property prediction (MPP). Most prior applications of deep learning to MPP have paid only limited attention to HPO, thus resulting in suboptimal values of predicted properties. To improve the efficiency and accuracy of deep learning models for MPP, we must optimize as many hyperparameters as possible and choose a software platform to enable the parallel execution of HPO. We compare the random search, Bayesian optimization, and hyperband algorithms, together with the Bayesian-hyperband combination within the software packages of KerasTuner and Optuna for HPO. We conclude that the hyperband algorithm, which has not been used in previous MPP studies, is most computationally efficient; it gives MPP results that are optimal or nearly optimal in terms of prediction accuracy. Based on our case studies, we recommend the use of the Python library KerasTuner for HPO.
深度神经网络超参数调整方法,用于高效准确的分子特性预测
本文介绍了一种用于分子性质预测(MPP)的深度神经网络超参数优化(HPO)方法。此前,深度学习在 MPP 中的应用大多只对 HPO 给予了有限的关注,从而导致预测的属性值未达标。为了提高深度学习模型用于 MPP 的效率和准确性,我们必须尽可能多地优化超参数,并选择一个能够并行执行 HPO 的软件平台。我们比较了随机搜索、贝叶斯优化和超带算法,以及用于 HPO 的 KerasTuner 和 Optuna 软件包中的贝叶斯-超带组合。我们得出的结论是,超带算法(以前的 MPP 研究中未使用过)的计算效率最高;就预测精度而言,它给出的 MPP 结果是最优或接近最优的。基于我们的案例研究,我们建议使用 Python 库 KerasTuner 进行 HPO。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Chemical Engineering
Computers & Chemical Engineering 工程技术-工程:化工
CiteScore
8.70
自引率
14.00%
发文量
374
审稿时长
70 days
期刊介绍: Computers & Chemical Engineering is primarily a journal of record for new developments in the application of computing and systems technology to chemical engineering problems.
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