The poor predictive accuracy of current computational enzyme design methods has led to low success rates of producing highly active variants that target non-natural substrates. In this report, a quantitative assessment approach based on molecular dynamics (MD) simulations was developed to eliminate false-positive enzyme designs at the computational stage. Taking cephradine hydrolase as an example, the apparent Michaelis binding constant (K_m) and catalytic efficiency (k_cat/K_m) of designed variants were correlated with binding free energies and activation energy barriers, respectively, as calculated by molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) methods with explicit water considered based on general MD simulation protocols. The correlation results showed that both the MM/GBSA and MM/PBSA methods with a protein dielectric constant (ε_p = 4) could rank the variants well based on the predicted binding free energies between enzyme and the substrate. Furthermore, the activation energy barriers calculated by the MM/PBSA method with an ε_p = 24 correlated well with k_cat/K_m. Thus, false-positive variants obtained by the enzyme design program PRODA were eliminated prior to experimentation. Therefore, MD simulation-based quantitative assessment of designed variants greatly enhanced the predictive accuracy of computational enzyme design tools and should facilitate the construction of artificial enzymes with high catalytic activities toward non-natural substrates.