Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences between molecules, limiting their ability to learn from smaller datasets and to directly compare the anticipated properties of two molecules. Many drug and material development tasks would benefit from an algorithm that can directly compare two molecules to guide molecular optimization and prioritization, especially for tasks with limited available data. Here, we develop DeepDelta, a pairwise deep learning approach that processes two molecules simultaneously and learns to predict property differences between two molecules from small datasets. On 10 ADMET benchmark tasks, our DeepDelta approach significantly outperforms two established molecular machine learning algorithms, the directed message passing neural network (D-MPNN) ChemProp and Random Forest using radial fingerprints, for 70% of benchmarks in terms of Pearson’s r, 60% of benchmarks in terms of mean absolute error (MAE), and all external test sets for both Pearson’s r and MAE. We further analyze our performance and find that DeepDelta is particularly outperforming established approaches at predicting large differences in molecular properties and can perform scaffold hopping. Furthermore, we derive mathematically fundamental computational tests of our models based on mathematical invariants and show that compliance to these tests correlates with overall model performance — providing an innovative, unsupervised, and easily computable measure of expected model performance and applicability. Taken together, DeepDelta provides an accurate approach to predict molecular property differences by directly training on molecular pairs and their property differences to further support fidelity and transparency in molecular optimization for drug development and the chemical sciences.

中文翻译：

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DeepDelta：通过深度学习预测分子衍生物的 ADMET 改进

已建立的分子机器学习模型将单个分子作为输入来预测其生物、化学或物理特性。然而，此类算法需要大型数据集，并且尚未针对预测分子之间的性质差异进行优化，限制了它们从较小数据集学习以及直接比较两个分子的预期性质的能力。许多药物和材料开发任务将受益于可以直接比较两个分子以指导分子优化和优先级排序的算法，特别是对于可用数据有限的任务。在这里，我们开发了 DeepDelta，这是一种成对深度学习方法，可以同时处理两个分子，并学习从小数据集中预测两个分子之间的属性差异。在 10 个 ADMET 基准任务中，我们的 DeepDelta 方法显着优于两种已建立的分子机器学习算法，即定向消息传递神经网络 (D-MPNN) ChemProp 和使用径向指纹的随机森林，对于 Pearson r 基准而言，70% 的基准为 60%平均绝对误差 (MAE) 方面的基准，以及 Pearson r 和 MAE 的所有外部测试集。我们进一步分析了我们的性能，发现 DeepDelta 在预测分子特性的巨大差异方面尤其优于现有方法，并且可以执行支架跳跃。此外，我们基于数学不变量对模型进行了数学基础计算测试，并表明遵守这些测试与整体模型性能相关——为预期模型性能和适用性提供了一种创新的、无监督的、易于计算的测量方法。总而言之，DeepDelta 提供了一种通过直接训练分子对及其属性差异来预测分子属性差异的准确方法，以进一步支持药物开发和化学科学的分子优化的保真度和透明度。