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：水溶性是一个有价值但具有挑战性的预测特性。使用第一性原理方法计算溶解度需要考虑熵与焓的竞争效应，导致计算时间长而精度相对较低。基于数据驱动的方法（如深度学习）在提高精度和计算效率的同时，通常缺乏不确定性量化。此外，任何计算技术的易用性仍是问题，这使得基团贡献法持续流行。本研究通过开发一个在静态网站（无需服务器）上运行的具有预测不确定性的深度学习模型来解决这些问题。该方法将计算需求转移至网站访问者端，无需安装、无需支付和维护服务器费用。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兼顾不确定性与易用性的分子属性预测模型。相关代码发布于https://github.com/ur-whitelab/mol.dev，模型可通过https://mol.dev使用。