Moving a candidate chemical from lab to bedside is still a famously expensive, complicated, and failure-prone process. Traditional prediction algorithms, meanwhile, struggle to navigate an ever-expanding chemical environment as modern pharmaceutics becomes more data-rich. In response, we present a Quantum Genetic Programming (QGP) framework designed to predict the aqueous solubility (LogS) of tiny organic compounds. Solubility, of course, determines absorption, distribution, and, eventually, bioavailability of any potential medicine. QGP, which draws on quantum computation principles, produces transparent linear equations with significantly lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) than classical machine-learning pipelines or standard genetic programming—approaches that frequently require elaborate preprocessing and exhaustive hyper-parameter tuning. Based on cheminformatics, the approach not only simplifies solubility prediction but also exceeds known benchmarks, propelling computational drug development research forward. By incorporating Quantum-Inspired evolutionary search into molecular property estimation, the current study provides the framework for prediction tools that are more accurate, scalable, and interpretable in the early stages of drug design.