Implicit Solvent Models and Their Applications in Biophysics

Solvation plays a critical role in determining the structural, dynamic, and functional behavior of biomolecules. Implicit solvent models, which approximate the solvent as a continuum dielectric medium, offer computationally efficient alternatives to explicit solvent simulations while capturing key aspects of solvation energetics. This review presents a comprehensive overview of implicit solvent models, spanning classical electrostatic formulations such as the Poisson–Boltzmann and Generalized Born equations, to quantum-based continuum approaches including PCM, COSMO, and the SMx family. We explore the theoretical underpinnings of these models, their treatment of polar and nonpolar contributions to solvation free energy, and extensions such as hybrid methods and machine learning-augmented approaches. Special attention is given to modern developments like AGBNP2, ABSINTH, GBNSR6, and COSMO-RS, as well as recent advances in modeling intrinsically disordered proteins (IDPs), protein–ligand interactions, and nucleic acid dynamics. Applications in drug discovery, molecular biophysics, and electronic structure prediction are highlighted. We also discuss current limitations, including the treatment of solvent entropy, ion-specific effects, and parameter sensitivity, and propose future directions involving AI integration and multiscale modeling. By critically evaluating implicit solvent models across theoretical and applied dimensions, this work aims to guide in selecting appropriate frameworks for simulating solvation phenomena in biological systems.