Modeling macromolecular thermodynamic properties, such as stability, dynamics and interactions, is essential for revealing details of biochemical processes occurring in the cell and further for figuring out what molecular effects are causing diseases. Among the forces and energies manifested at atomic level of details, the electrostatics is one of the most prominent, because all atoms carry partial charge and the electrostatic force is a long-range force dominating all other forces at long distances. Particularly, the electrostatics is the driving force for pH- dependence of macromolecular stability and activity. However, modeling electrostatic forces and energies of biological macromolecules is highly nontrivial because of their irregular shape, the conformational changes occurring during the corresponding process and the presence of water phase. An efficient way to overcome such a complexity is to consider water phase and macromolecule(s) on the same footage as continuum media with inhomogeneous polarizability. This is the approach currently available ONLY in DelPhi package, where internal cavities, low density macromolecular regions and surface waters are modeled via inhomogeneous Gaussian and super-Gaussian dielectric functions. In a series of works, it has been shown that this approach outperforms the traditional two-dielectric model and delivers ensemble-averaged quantities. With this proposal we are seeking support to continue maintaining and developing DelPhi suite and associated resources. In parallel with continuous support that we provide to our users (more than 7,000 registered users), we plan to develop many new features in DelPhi as: (1) enabling DelPhi to handle molecular dynamics (MD) generated trajectories by the most frequently used MD packages as NAMD, CHARMM, GROMACS and AMBER; (2) upgrading DelPhi to model geometrical properties as volume and molecular surface, which combined with (1) will allow DelPhi to carry MM/PBSA calculations without third party software in fast and efficient manner; (3) estimation of entropy, (4) novel energy partitioning and (5) residue-specific Gaussian-based dielectric function. These new developments will be used to improve and completely re-design DelPhi associated resources as SAAFEC, SAAMBE, and SAMPDI, which are methods for predicting the change of the protein folding, protein binding, and protein-RNA/DNA binding free energies due to mutations, respectively. In parallel, machine learning (ML) approaches will be utilized to improve SAAFEC, SAAMBE and SAMPDI performance and a new feature “Gaussian total density” will be implemented in the ML protocols.