Enhancing MM/P(G)BSA Methods: Integration of Formulaic Entropy for Improved Binding Free Energy Calculations and Virtual Screening Efficiency

Balancing computational efficiency and precision, MM/P(G)BSA methods have been widely employed in the estimation of binding free energies within biological systems. However, the entropy contribution to the binding free energy is often neglected in MM/P(G)BSA calculations, due to the computational cost of conventional methods such as normal mode analysis (NMA). In this work, we develop an enhanced MM/P(G)BSA method by incorporating the entropy effect using a formulaic entropy. Extensive benchmarking reveals that the integration of formulaic entropy systematically elevates the performance of both MM/PBSA and MM/GBSA without incurring additional computational expenses. Notably, MM/PBSA_S, augmented with formulaic entropy, surpasses all other MM/P(G)BSA methods across a spectrum of datasets. Furthermore, the incorporation of MM/PBSA_S into a workflow can yield significantly improved results for the virtual screening, marked by a considerable enhancement in the enrichment factor. Our investigation furnishes a valuable and practical MM/P(G)BSA method, optimizing binding free energy calculations for a variety of biological systems.