Molecular docking plays an important role in the protein target identification by prioritizing probable druggable proteins using docking energies. Due to the limitations of docking scoring schemes, there arises a need for structure-based approaches to acquire confidence in theoretical binding affinities. In this direction, we present here a receptor (protein)-based approach to predict probable protein targets using a small molecule of interest. We adopted a reverse approach wherein the ligand pharmacophore features were used to decipher interaction complementary amino acids of protein cavities (a pseudoreceptor) and expressed as queries to match the cavities or binding sites of the protein dataset. These pseudoreceptor-based pharmacophore queries were used to estimate total probabilities of each protein cavity thereby representing the ligand binding efficiency of the protein. We applied this approach to predict 3 experimental protein targets among 28 Zea mays structural data using 3 co-crystallized ligands as inputs and compared its effectiveness using conventional docking results. We suggest that the combination of total probabilities and docking energies increases the confidence in prioritizing probable protein targets using docking methods. These prediction hypotheses were further supported by DrugScoreX (DSX) pair potential calculations and molecular dynamic simulations.