  Prediction of solubility has been a complex and challenging physiochemical
problem that has tremendous implications in the chemical and pharmaceutical
industry. Recent advancements in machine learning methods have provided great
scope for predicting the reliable solubility of a large number of molecular
systems. However, most of these methods rely on using physical properties
obtained from experiments and or expensive quantum chemical calculations. Here,
we developed a method that utilizes a graphical representation of
solute-solvent interactions using `MolMerger', which captures the strongest
polar interactions between molecules using Gasteiger charges and creates a
graph incorporating the true nature of the system. Using these graphs as input,
a neural network learns the correlation between the structural properties of a
molecule in the form of node embedding and its physiochemical properties as
output. This approach has been used to calculate molecular solubility by
predicting the Log solubility values of various organic molecules and
pharmaceuticals in diverse sets of solvents.
