Of course, for most practical applications, in vivo or in vitro, DNA exists in an aqueous environment. Further, DNA's interactions with its solvent environment are crucial for influencing its properties . Therefore, we extend our previously developed gas-phase DNA model to describe solvated DNA with the solvent interactions modeled explicitly. Machine learning is well-suited to this task for two reasons: First, the main challenge with explicit solving modeling is that system sizes quickly become computationally intractable due to the sheer numbers of individual solvent molecules that need to be included. Machine learning models have been shown to break through traditional scaling limits and can handle large-scale systems that are well-outside the bounds of conventional methods . The second reason is that density-based machine learning models can accurately describe the prominent types of interaction for solvated DNA systems. Our previous gas-phase DNA model showed that internal DNA interactions can be accounted for , and machine learning models have been shown to capture non-covalent interactions , which is the dominant type of interaction between DNA and solvent. Solvent interactions have also been shown to be important in DNA binding studies. In non-contact or "indirect readout" binding, a binding agent targets specific segments of DNA without directly reading in the base sequence . Instead, the binding agent recognizes the target based on properties such as the electrostatic potential that can be influenced by the base sequence. For example, DNA systems with A-tracts (base sequence segments with four or more consecutive A bases) exhibit unique structural features, including intensified bending of the helix as well as narrowed minor grooves . The narrowness of the minor groove effectuates an enhanced negative electrostatic potential around the A-tract sequence, which is thought to be the mechanism for site-specific binding of many minor-groove binding proteins . This phenomenon has been studied with coarse-grained implicit solvent models, where the enhancement of the electrostatic potential is shown to be driven by solvation . An explicit solvent model that could study solvation effects on a finer scale would thus be useful for studying such phenomena in greater detail.