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 https://github.com/ur-whitelab/mol.dev, and the model is usable at https://mol.dev.

翻译：水溶性是一项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，导致计算时间长且准确率相对较低。基于数据驱动的方法（如深度学习）具有更高的准确率和计算效率，但通常缺乏不确定性量化。此外，任何计算技术的易用性仍是关注点，这导致基团贡献法持续流行。在本工作中，我们通过一个在静态网站（无需服务器）上运行的带预测不确定性的深度学习模型解决了这些问题。该方法将计算需求转移至网站访问者端，无需安装软件，从而免除了服务器购买和维护成本。我们的模型在溶解度预测中取得了令人满意的结果。此外，我们展示了如何构建平衡不确定性与易用性的分子性质预测模型。代码见 https://github.com/ur-whitelab/mol.dev，模型可于 https://mol.dev 使用。