We present the MORE-Q dataset using quantum-mechanical (QM) simulations for dimer systems composed of body odor volatilome (BOV) and olfactory receptors. The dataset contains abundant QM properties of diverse BOV-receptor systems, both in the gas phase and when deposited on a graphene surface. After analyzing the property space spanned by MORE-Q, we observed flexibility when searching for a dimer configuration with a desired set of electronic binding features. To gain insights into the complex interplay between these sensing properties, an ensemble learning method (XGBoost) was constructed for the fast evaluation of BOV adsorption behavior using only the dimer configurations properties. The results show a significant increase in model performance by adding multiple conformers to the training procedure, and SHAP analysis identifies the most relevant descriptors for predicting the binding features. Our work provides valuable insights into the the sensing mechanism of BOV molecules and paves the way for the computational design of receptors with targeted sensitivity and selectivity.