推进分子机器学习表示：立体电子效应注入的分子图
Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs
AI总结：
本研究通过立体电子效应将量子化学信息注入分子图，利用双图神经网络增强分子表示，提升性质预测性能，并实现向大分子（如蛋白质）的外推，同时提供在线工具。
摘要：
分子表示是我们理解物理世界的关键要素，也是现代分子机器学习的基础。以往的分子机器学习模型采用了字符串、指纹、全局特征和简单的分子图，这些本质上都是信息稀疏的表示。然而，随着预测任务复杂度的增加，分子表示需要编码更高保真度的信息。本工作引入了一种新方法，通过立体电子效应将富含量子化学的信息注入分子图，从而增强表达性和可解释性。通过定制的双图神经网络工作流学习预测立体电子效应注入的表示，使其能够应用于任何下游分子机器学习任务，而无需昂贵的量子化学计算。我们表明，显式添加立体电子信息显著提升了用于分子性质预测的消息传递二维机器学习模型的性能。我们还表明，在小分子上训练得到的表示能够准确外推到更大的分子结构，为之前难以处理的系统（如整个蛋白质）提供轨道相互作用的化学洞察，开辟了分子设计的新途径。最后，我们开发了一个网络应用程序（simg.cheme.cmu.edu），用户可以在其中快速探索自己分子系统的立体电子信息。
英文摘要：
Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have employed strings, fingerprints, global features, and simple molecular graphs that are inherently information-sparse representations. However, as the complexity of prediction tasks increases, the molecular representation needs to encode higher fidelity information. This work introduces a novel approach to infusing quantum-chemical-rich information into molecular graphs via stereoelectronic effects, enhancing expressivity and interpretability. Learning to predict the stereoelectronics-infused representation with a tailored double graph neural network workflow enables its application to any downstream molecular machine learning task without expensive quantum chemical calculations. We show that the explicit addition of stereoelectronic information significantly improves the performance of message-passing 2D machine learning models for molecular property prediction. We show that the learned representations trained on small molecules can accurately extrapolate to much larger molecular structures, yielding chemical insight into orbital interactions for previously intractable systems, such as entire proteins, opening new avenues of molecular design. Finally, we have developed a web application (simg.cheme.cmu.edu) where users can rapidly explore stereoelectronic information for their own molecular systems.
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