The Adaptive Poisson-Boltzmann Solver (APBS) is a software package designed to solve the equations of continuum electrostatics for large biomolecular systems, addressing key challenges in understanding solvation and electrostatics in biomedical applications. Since its release in 2001, APBS has been continuously updated to incorporate new models and capabilities. This manuscript discusses recent additions to the APBS software, including: 1. **Poisson-Boltzmann Analytical and Semi-Analytical Solvers**: New analytical and semi-analytical solvers, PBAM and PB-SAM, respectively, provide accurate and efficient solutions to the Poisson-Boltzmann equation for biomolecular systems. 2. **Optimized Boundary Element Solver**: An optimized boundary element solver, TABI-PB, is now included in APBS, which focuses numerical effort on the interface between the molecule and the solvent, improving computational efficiency. 3. **Geometry-Based Geometric Flow Solvation Model**: A geometry-based geometric flow solvation model is introduced, which optimizes the solute-solvent interface and ensures self-consistent calculation of polar and nonpolar energetic contributions. 4. **Graph Theory-Based pK_a Algorithm**: A graph theory-based algorithm for determining pK_a values is implemented, enhancing the accuracy and reliability of pH-dependent protein-ligand binding studies. 5. **Improved Web-Based Visualization Tool**: An improved web-based visualization tool is provided to view electrostatic potentials, allowing for easier data analysis and interpretation. The article also covers the preparation of biomolecular structures using the PDB2PQR software, which assists in converting PDB files to PQR format, and the configuration of finite difference and finite element solvers in APBS. Additionally, it discusses the application of APBS in various fields such as molecular dynamics, protein-nanoparticle interactions, and biomolecular docking. The future of APBS is envisioned to be a modular and extensible framework that supports cloud-based resources and user-defined models.The Adaptive Poisson-Boltzmann Solver (APBS) is a software package designed to solve the equations of continuum electrostatics for large biomolecular systems, addressing key challenges in understanding solvation and electrostatics in biomedical applications. Since its release in 2001, APBS has been continuously updated to incorporate new models and capabilities. This manuscript discusses recent additions to the APBS software, including: 1. **Poisson-Boltzmann Analytical and Semi-Analytical Solvers**: New analytical and semi-analytical solvers, PBAM and PB-SAM, respectively, provide accurate and efficient solutions to the Poisson-Boltzmann equation for biomolecular systems. 2. **Optimized Boundary Element Solver**: An optimized boundary element solver, TABI-PB, is now included in APBS, which focuses numerical effort on the interface between the molecule and the solvent, improving computational efficiency. 3. **Geometry-Based Geometric Flow Solvation Model**: A geometry-based geometric flow solvation model is introduced, which optimizes the solute-solvent interface and ensures self-consistent calculation of polar and nonpolar energetic contributions. 4. **Graph Theory-Based pK_a Algorithm**: A graph theory-based algorithm for determining pK_a values is implemented, enhancing the accuracy and reliability of pH-dependent protein-ligand binding studies. 5. **Improved Web-Based Visualization Tool**: An improved web-based visualization tool is provided to view electrostatic potentials, allowing for easier data analysis and interpretation. The article also covers the preparation of biomolecular structures using the PDB2PQR software, which assists in converting PDB files to PQR format, and the configuration of finite difference and finite element solvers in APBS. Additionally, it discusses the application of APBS in various fields such as molecular dynamics, protein-nanoparticle interactions, and biomolecular docking. The future of APBS is envisioned to be a modular and extensible framework that supports cloud-based resources and user-defined models.