Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. Data-driven approaches, such as deep learning, offer improved accuracy and computational efficiency but typically lack uncertainty quantification. Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods. In this work, we addressed these problems with a deep learning model with predictive uncertainty that runs on a static website (without a server). This approach moves computing needs onto the website visitor without requiring installation, removing the need to pay for and maintain servers. Our model achieves satisfactory results in solubility prediction. Furthermore, we demonstrate how to create molecular property prediction models that balance uncertainty and ease of use. The code is available at \url{https://github.com/ur-whitelab/mol.dev}, and the model is usable at \url{https://mol.dev}.

翻译：水溶解度是一个有价值但难以预测的性质。使用第一性原理方法计算溶解度需要考虑熵和焓的竞争效应，导致计算时间长而精度相对较低。数据驱动方法（如深度学习）提供了更高的精度和计算效率，但通常缺乏不确定性量化。此外，易用性仍是任何计算技术面临的问题，这导致基团贡献法持续流行。在本工作中，我们通过一个具有预测不确定性的深度学习模型解决了这些问题，该模型可在静态网站（无服务器）上运行。这种方法将计算需求转移到网站访问者端，无需安装即可运行，消除了付费维护服务器的需求。我们的模型在溶解度预测上取得了满意结果。此外，我们展示了如何创建平衡不确定性与易用性的分子性质预测模型。代码可在\url{https://github.com/ur-whitelab/mol.dev}获取，模型可在\url{https://mol.dev}使用。