The simulated DPPC results suggest that the LJSPM method can serve as an effective means for deriving LJ parameters of CG particles. In the literature, numerous CG models have been developed using a top-down approach. For example, CG models for alkanes have been parameterized based on experimental properties like density and enthalpy of vaporization. In this work, we aim to develop accurate CG models based on a bottom-up strategy, with a particular emphasis on utilizing the LJSPM method to determine the LJ parameters of CG models. Furthermore, we noticed that the previously developed LJSPM tends to underestimate short-range LJ repulsion between particles, leading to a reduction in the effective volume of CG particles and thus substantial discrepancies in solvation energy, density, and radial distribution functions. We develop a novel and efficient correction method called the Effective Volume Correction (EVC) to systematically correct the LJ volume exclusion. Based on the EVC-based LJSPM method, which now possesses clear physical meaning, we achieve significant improvements in accuracy when describing the solvation free energy, radial distribution functions, and density of organic molecules and their mixtures.