计算机科学
超参数
机器学习
工作流程
软件
水准点(测量)
人工智能
多样性(控制论)
人工神经网络
财产(哲学)
贝叶斯优化
数据库
程序设计语言
哲学
大地测量学
认识论
地理
作者
Esther Heid,Kevin P. Greenman,Yunsie Chung,Shih‐Cheng Li,David Graff,Florence H. Vermeire,Haoyang Wu,William H. Green,Charles J. McGill
标识
DOI:10.1021/acs.jcim.3c01250
摘要
Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.
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