Project Summary. Progress in modern bio-molecular sciences, from structural biology to structure-based drug design, is greatly accelerated by methods of atomic-level modeling and simulations that bridge the gap between theory and experiment. The so-called implicit solvation model can provide critical advantages of speed and versatility through representing the effects of solvent – often the most computationally expensive part of such simulations – in an approximate manner. The resulting speed-up of modeling efforts is critical in many areas such as protein folding or protein-ligand docking. However, the accuracy of these fast models does not reach the standard of the more traditional, but computationally very demanding explicit solvent approach. In particular, even sophisticated implicit solvation models are unable to emulate explicit solvation effects with chemical accuracy (errors less than 1 kcal/mol), simultaneously for small drug-like molecules and amino-acids – the two key building blocks of every bio-medically relevant atomistic simulation. In general, chemical accuracy is a prerequisite for quantitative in-silico drug design. As a result, reliability of the practical, fast implicit solvation models remains low for many bio-medically relevant problems such as protein-ligand binding. Here, the accuracy limitation will be addressed in a novel, systematic way; advantages of the new implicit solvation models will be demonstrated within the context of bio-medically relevant applications. This supplemental funding will allow us to explore, test, and fine-tune the new methodology in the context of protein-drug binding directly relevant to fast-track development of novel therapeutics for the on-going COVID disease and future pandemics.