Sequence-defined oligocarbamates binding in solution: multiscale modeling and experimental study

Our understanding of the rules of DNA base pairing has opened new avenues of research in materials design. The deployment of DNA technologies on a commercial scale, however, is limited due to constraints in processing conditions such as the need for operating in aqueous solutions. Hence, it is of interest to develop synthetic molecules that mimic the programmable hybridization strength of DNA strands but are able to selectively associate in organic solvents. We present a computational framework to facilitate the rational design of synthesizable sequence-defined oligocarbamates (SeDOC) for improved fidelity and stability. We show that our novel SeDOCs bind specifically through sequence-defined binding sites. This specific binding is both enthalpically and entropically favorable. We show through multiscale molecular simulations that the positive change in entropy associated with the binding of two complementary SeDOC arises from the competition between intramolecular and intermolecular binding. By augmenting the multiscale molecular simulations platform with a machine learning framework, we are able to efficiently unveil correlations between sequence space and binding thermodynamics to propose molecular design criteria to achieve target binding characteristic. In the future, we plan to leverage our sequence-defined molecules in the self-assembly of nanoparticles for photonic and electronic applications.