# Next generation implicit solvation for atomistic modeling

> **NIH NIH R01** · VIRGINIA POLYTECHNIC INST AND ST UNIV · 2024 · $44,647

## Abstract

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.

## Key facts

- **NIH application ID:** 11035689
- **Project number:** 3R01GM144596-03S1
- **Recipient organization:** VIRGINIA POLYTECHNIC INST AND ST UNIV
- **Principal Investigator:** ALEXEY VLAD ONUFRIEV
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $44,647
- **Award type:** 3
- **Project period:** 2022-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11035689

## Citation

> US National Institutes of Health, RePORTER application 11035689, Next generation implicit solvation for atomistic modeling (3R01GM144596-03S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11035689. Licensed CC0.

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