LAUSR

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.