Uncertainty-Aware Yield Prediction with Multimodal Molecular Features
Uncertainty-Aware Yield Prediction with Multimodal Molecular Features
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DOI：
10.1609/aaai.v38i8.28668
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发表时间：
2024-03
影响因子：
15.9
通讯作者：
Jiayuan Chen;Kehan Guo;Zhen Liu;O. Isayev;Xiangliang Zhang
中科院分区：
文献类型：
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作者：
Jiayuan Chen;Kehan Guo;Zhen Liu;O. Isayev;Xiangliang Zhang
预测化学反应产率是高效化学合成的关键，该领域专注于创造用于多种用途的新型化合物。产率预测需要反应的准确表示，以预测实际的转化率。然而，在现实世界的情况下广播的不确定性问题，禁止目前的模型，以擅长这项任务，由于产量活动的高敏感性和产量测量的不确定性。现有的模型通常使用单模态特征表示，如分子指纹，SMILES序列或分子图，这不足以捕捉反应中分子的复杂相互作用和动态行为。在本文中，我们提出了一个先进的不确定性感知多模态模型（UAM）来应对这些挑战。我们的方法通过包含序列表示、分子图和专家定义的化学反应特征来无缝集成来自多种模式的数据源，以全面表示反应。此外，我们解决了模型和基于数据的不确定性，改进了模型的预测能力。在三个数据集上的广泛实验，包括两个高通量实验（HTE）数据集和一个化学家构建的酰胺偶联反应数据集，表明UAM优于最先进的方法。代码和使用的数据集可在https://github.com/jychen229/Multimodal-reaction-yield-prediction上获得。
Predicting chemical reaction yields is pivotal for efficient chemical synthesis, an area that focuses on the creation of novel compounds for diverse uses. Yield prediction demands accurate representations of reactions for forecasting practical transformation rates. Yet, the uncertainty issues broadcasting in real-world situations prohibit current models to excel in this task owing to the high sensitivity of yield activities and the uncertainty in yield measurements. Existing models often utilize single-modal feature representations, such as molecular fingerprints, SMILES sequences, or molecular graphs, which is not sufficient to capture the complex interactions and dynamic behavior of molecules in reactions. In this paper, we present an advanced Uncertainty-Aware Multimodal model (UAM) to tackle these challenges. Our approach seamlessly integrates data sources from multiple modalities by encompassing sequence representations, molecular graphs, and expert-defined chemical reaction features for a comprehensive representation of reactions. Additionally, we address both the model and data-based uncertainty, refining the model's predictive capability. Extensive experiments on three datasets, including two high throughput experiment (HTE) datasets and one chemist-constructed Amide coupling reaction dataset, demonstrate that UAM outperforms the state-of-the-art methods. The code and used datasets are available at https://github.com/jychen229/Multimodal-reaction-yield-prediction.