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Secondary formamides are widely encountered in biology and exist as mixtures of both cis and trans isomers. Here, we assess hydrophilicity differences between isomeric formamides through direct competition experiments. Formamides bearing long aliphatic chains were sequestered in a water-soluble molecular container having a hydrophobic cavity with an end open to the aqueous medium. NMR spectroscopic experiments reveal a modest preference (<1 kcal/mol) for aqueous solvation of the trans formamide terminals over the cis isomers. With diformamides, the supramolecular approach allows staging of intramolecular competition between short-lived species with subtle differences in hydrophobic properties. 

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure–activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.