Empirical force fields are essential tools for studying biological macromolecules at the atomic level, enabling the interpretation of experimental data and revealing information not accessible through experiments. These methods rely on the quality of force fields and algorithmic advancements to accurately reproduce experimental observables. This review discusses the development and application of empirical force fields for proteins, nucleic acids, lipids, and carbohydrates, along with challenges in heterogeneous systems and transferability to pharmacologically relevant molecules. Key issues include the accuracy of force fields, parameter optimization, and the treatment of nonbonded interactions. The potential energy function includes terms for bonds, angles, dihedrals, and nonbonded interactions, with parameters determined through quantum mechanical (QM) calculations. Additive force fields, which use Coulombic and Lennard-Jones terms, have been widely used, though they lack explicit treatment of electronic polarizability. Polarizable force fields, which account for electronic polarization, offer improved accuracy but face challenges in transferring parameters between force fields. Combining rules for nonbonded interactions, such as the Lorentz-Berthelot rules, are crucial for parameter transfer. The treatment of solvation, including explicit and implicit models, is vital for accurate simulations. Long-range interactions are handled through truncation, Ewald summation, and other methods. Force field optimization involves QM data for target parameters, with challenges in parameter correlation. Proper optimization of nonbonded parameters is essential for accurate simulations, and the choice of water model is critical for compatibility. Overall, empirical force fields remain important for biomolecular simulations, with ongoing developments in polarizable and more accurate force fields.Empirical force fields are essential tools for studying biological macromolecules at the atomic level, enabling the interpretation of experimental data and revealing information not accessible through experiments. These methods rely on the quality of force fields and algorithmic advancements to accurately reproduce experimental observables. This review discusses the development and application of empirical force fields for proteins, nucleic acids, lipids, and carbohydrates, along with challenges in heterogeneous systems and transferability to pharmacologically relevant molecules. Key issues include the accuracy of force fields, parameter optimization, and the treatment of nonbonded interactions. The potential energy function includes terms for bonds, angles, dihedrals, and nonbonded interactions, with parameters determined through quantum mechanical (QM) calculations. Additive force fields, which use Coulombic and Lennard-Jones terms, have been widely used, though they lack explicit treatment of electronic polarizability. Polarizable force fields, which account for electronic polarization, offer improved accuracy but face challenges in transferring parameters between force fields. Combining rules for nonbonded interactions, such as the Lorentz-Berthelot rules, are crucial for parameter transfer. The treatment of solvation, including explicit and implicit models, is vital for accurate simulations. Long-range interactions are handled through truncation, Ewald summation, and other methods. Force field optimization involves QM data for target parameters, with challenges in parameter correlation. Proper optimization of nonbonded parameters is essential for accurate simulations, and the choice of water model is critical for compatibility. Overall, empirical force fields remain important for biomolecular simulations, with ongoing developments in polarizable and more accurate force fields.