With more and more protein structures being identified, as well as rapid developments in the pharmaceutical industry, computational biology is increasingly playing a vital role in biological data retrieval, analysis, and visualization. Cost-effective approaches to identify the docking potential between proteins and ligands are highly desirable. Successful docking of ligands and proteins is the goal in a virtual screening process. The docking methods involve system representation, conformational searching and matching, and ranking mechanisms. Docking algorithms are typically very time consuming. But in the early stages of drug discovery, there is generally a need for a massive virtual screening process to identify potential drug candidates or pharmacophores. This motivates us to develop a ligand-protein docking algorithm with emphasis on high performance. The docking of ligands and proteins occurs on the surfaces of the bio-molecules. Hence, it is important to have good representations of the protein surface. In this research, a partition-based molecular implicit surface (PMIS) is proposed to represent the bio-molecular (protein) solvent-accessible surface (SAS). The implicit model and space partition algorithm make PMIS unique in determining the point-surface relationship. Therefore, PMIS can facilitate structural research such as interactive or geometric docking of proteins and ligands.