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化学prop:用于化学性质预测的机器学习包

Esther Heid^1,2, Kevin P Greenman¹, Yunsie Chung¹

¹Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Journal of chemical information and modeling

|December 26, 2023

概括

开源软件Chemprop用于使用定向消息传递神经网络 (D-MPNNs) 进行分子性质预测,现在支持反应,光谱和不确定性量化. 这种增强的工具在各种化学预测任务中取得了最先进的结果.

科学领域:

计算化学计算化学
机器学习在化学中的应用
药物发现软件药物发现软件

背景情况:

深度学习模型,特别是定向消息传递神经网络 (D-MPNNs),对于预测分子性质越来越重要.
现有的软件解决方案往往缺乏非专家操作所需的多功能性和用户友好性.
对可访问的开源工具的需求越来越大,以利用机器学习在化学研究中发挥作用.

研究的目的:

引入Chemprop软件包的显著增强,扩大其能力超出了基本的分子性质预测.
为用户提供先进的功能,用于不确定性量化,转移学习和处理复杂的化学数据,如反应和光谱.
在各种具有挑战性的化学预测任务上使用更新的Chemprop训练的D-MPNN模型的性能进行基准测试.

主要方法:

在Chemprop中实现新的功能,包括支持多分子性质,反应,原子/键层预测和光谱数据.
将不确定性量化和校准方法与相关绩效指标相结合.
开发预训练和转移学习工作流程,以及改进的超参数优化和损失函数和分子特征的定制选项.

主要成果:

在基准数据集 (MoleculeNet, SAMPL) 上,Chemprop 展示了先进的性能,用于预测关键分子性质.
具体的成就包括高精度预测水-醇分区系数,反应屏障高度,原子部分电荷和吸收光谱.
改进后的软件可以为D-MPNN模型在各种化学预测问题上提供即时的训练.

结论:

更新后的Chemprop软件提供了一个强大的,用户友好的开源解决方案,用于使用D-MPNNs进行先进的分子性质预测.
它的扩展功能,包括反应和光谱分析,使其成为计算化学专家和非专家研究人员的宝贵工具.
化学的强大性能和多功能性有助于在化学发现和开发中更广泛地采用机器学习.

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