Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available. Scientific contribution Our research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.

中文翻译：

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使用机器学习预测不同温度下药物在二元溶剂混合物中的溶解度


药物溶解度是药物开发过程中的一个重要参数，但测量起来往往很繁琐且具有挑战性，尤其是对于昂贵的药物或少量可用的药物。为了缓解这些挑战，机器学习 （ML） 已被应用于预测药物溶解度作为一种替代方法。然而，大多数现有的 ML 研究都集中在水溶性和/或特定温度下的溶解度的预测上，这限制了模型在药物开发中的适用性。为了弥合这一差距，我们编译了一个包含 27,000 个溶解度数据点的数据集，包括在不同温度下在一系列二元溶剂混合物中测量的小分子溶解度。接下来，在此数据集上训练一组 ML 模型，并使用贝叶斯优化调整其超参数。由此产生的性能最好的模型，即梯度提升决策树（轻度梯度提升机和极端梯度提升），在保持集上实现了 LogS（S 以 g/100 g 为单位）的平均绝对误差 （MAE） 为 0.33。这些模型通过一项前瞻性研究进一步验证，其中模型预测了四种药物分子的溶解度，然后通过内部溶解度实验进行了验证。这项前瞻性研究表明，这些模型准确预测了不同温度下溶质在特定二元溶剂混合物中的溶解度，特别是对于其特征与数据集中溶质紧密对齐的药物（LogS 的 MAE < 0.5）。为了支持未来的研究并促进该领域的进步，我们公开了数据集和代码。 科学贡献 我们的研究通过利用 ML 和独特的综合数据集，推进了预测小分子溶解度的最新技术。与主要关注固定温度下在水性溶剂中的溶解度的现有 ML 研究不同，我们的工作能够在较宽的温度范围内预测药物在各种二元溶剂混合物中的溶解度，为实际制药应用的溶解度建模提供实用见解。这些进步以及开放获取的数据集和代码为药物开发过程中的重要步骤提供支持，包括新分子发现、药物分析和配方。