Abstract Alternative binders require next-generation chemical admixtures, but discovery of such compounds is typically achieved through extensive iterative testing that does not ensure optimal solutions. Here, the use of cheminformatics, a data-driven approach used extensively in drug discovery, is demonstrated to identify new set retarders from small datasets for calcium sulfoaluminate (CSA) cements. Based on a sparse training set of 23 molecules containing polar and anionic functional groups, the cheminformatics approach was used to develop a predictive model relating chemical structure to the retarding capability. Then structures of 500,000 compounds were downloaded from a public database, and 365 were predicted to extend set time beyond 1 h. Among these, glyphosate is a commodity chemical that was found to impart a set time of 55 min. This cheminformatics approach could be used to develop structure-function relationships and perform rapid virtual screening of chemical admixtures to identify novel high-performance chemical admixtures.

中文翻译：

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化学外加剂加速设计的化学信息学

摘要 替代粘合剂需要下一代化学混合物，但此类化合物的发现通常是通过广泛的迭代测试实现的，并不能确保最佳解决方案。在这里，化学信息学（一种广泛用于药物发现的数据驱动方法）被证明可以从硫铝酸钙 (CSA) 水泥的小数据集中识别新的缓凝剂。基于包含极性和阴离子官能团的 23 个分子的稀疏训练集，化学信息学方法用于开发将化学结构与阻滞能力相关联的预测模型。然后从公共数据库下载 500,000 种化合物的结构，预计 365 种化合物的固化时间超过 1 小时。其中，草甘膦是一种商品化学品，发现其固化时间为 55 分钟。