Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The vast majority of presently adopted approaches for polymer screening rely on handcrafted chemostructural features extracted from polymer repeat units- a burdensome task as polymer libraries, which approximate the polymer chemical search space, progressively grow over time. Here, we demonstrate that directly "machine-learning" important features from a polymer repeat unit is a cheap and viable alternative to extracting expensive features by hand. Our approach- based on graph neural networks, multitask learning, and other advanced deep learning techniques- speeds up feature extraction by one to two orders of magnitude relative to presently adopted handcrafted methods without compromising model accuracy for a variety of polymer property prediction tasks. We anticipate that our approach, which unlocks the screening of truly massive polymer libraries at scale, will enable more sophisticated and large scale screening technologies in the field of polymer informatics.

中文翻译：

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具有多任务图神经网络的大规模聚合物信息学

基于人工智能的方法在筛选聚合物库方面变得越来越有效，直至筛选出可用于实验调查的选择。目前采用的绝大多数聚合物筛选方法都依赖于从聚合物重复单元中提取的手工化学结构特征——这是一项繁重的任务，因为近似聚合物化学搜索空间的聚合物库随着时间的推移逐渐增长。在这里，我们证明了直接从聚合物重复单元中“机器学习”重要特征是一种廉价且可行的替代手动提取昂贵特征的方法。我们的方法——基于图神经网络、多任务学习、和其他先进的深度学习技术 - 相对于目前采用的手工方法将特征提取速度提高一到两个数量级，而不会影响各种聚合物特性预测任务的模型准确性。我们预计，我们的方法可以大规模筛选真正大规模的聚合物库，将在聚合物信息学领域实现更复杂和更大规模的筛选技术。