BERT 学习（并教授）化学
BERT Learns (and Teaches) Chemistry
AI总结：
BERT通过学习分子结构中的功能基团和原子表示，用于预测化学性质和解决药物发现问题，并利用注意力可视化辅助化学研究。
摘要：
现代计算有机化学正变得越来越数据驱动。在这一领域仍有许多重要的未解决问题，例如给定反应物预测产物、药物发现和优化度量的分子合成，但近年来利用机器学习解决这些问题的努力也增加了。在本工作中，我们提出利用注意力机制从数据驱动的角度研究功能基团和其他影响分子性质的分子亚结构，使用基于变压器的模型（BERT）在分子字符串表示的数据集上进行研究，并分析其注意力头的行为。然后，我们将模型学习的功能基团和原子的表示应用于更小的数据集上，解决毒性、溶解性、药物样性和合成可及性等问题，使用学习到的表示作为图卷积和注意力模型在分子图结构上的特征，以及微调BERT。最后，我们提出利用注意力可视化作为帮助化学从业者和学生快速识别各种化学性质中重要亚结构的有用工具。
英文摘要：
Modern computational organic chemistry is becoming increasingly data-driven. There remain a large number of important unsolved problems in this area such as product prediction given reactants, drug discovery, and metric-optimized molecule synthesis, but efforts to solve these problems using machine learning have also increased in recent years. In this work, we propose the use of attention to study functional groups and other property-impacting molecular substructures from a data-driven perspective, using a transformer-based model (BERT) on datasets of string representations of molecules and analyzing the behavior of its attention heads. We then apply the representations of functional groups and atoms learned by the model to tackle problems of toxicity, solubility, drug-likeness, and synthesis accessibility on smaller datasets using the learned representations as features for graph convolution and attention models on the graph structure of molecules, as well as fine-tuning of BERT. Finally, we propose the use of attention visualization as a helpful tool for chemistry practitioners and students to quickly identify important substructures in various chemical properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
