# Accurate yet fast implicit solvation

> **NIH NIH R21** · VIRGINIA POLYTECHNIC INST AND ST UNIV · 2020 · $230,432

## Abstract

Project Summary. This proposal responds to PAR-17-046 “Exploratory Research for Technology
Development (R21)”. The goal of the proposal is to explore a novel way of constructing implicit solvation
models that can be as accurate as the standard explicit solvent models, but much faster.
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.
We will use a novel approach to systematically add most of the explicit solvation effects to the very basic, but
efficient implicit solvation framework of the Poisson model, with little computational overhead. We will explore
the possibility of adaptation of the new models for MD simulations. We have set high accuracy standards for
the new theory: chemical accuracy simultaneously for small drug-like molecules and amino-acids. Reaching
that goal is paramount for ushering in the next generation of implicit solvent models that can make a profound
difference in bio-medically relevant atomistic calculations.
Results will benefit the entire biomolecular modeling community by providing it with an approach to build new,
accurate and fast tools for atomistic simulation. Example of an immediate impact: Close to ”explicit solvent”
accuracy in protein-ligand binding calculations, but without the associated expense. Example of a long term
impact: A clear quantitative understanding of which of the many explicit solvent effects missing from the basic
continuum solvent description, are most/least important for accuracy of practical atomistic computations.

## Key facts

- **NIH application ID:** 9873052
- **Project number:** 5R21GM131228-02
- **Recipient organization:** VIRGINIA POLYTECHNIC INST AND ST UNIV
- **Principal Investigator:** ALEXEY VLAD ONUFRIEV
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $230,432
- **Award type:** 5
- **Project period:** 2019-03-01 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9873052

## Citation

> US National Institutes of Health, RePORTER application 9873052, Accurate yet fast implicit solvation (5R21GM131228-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9873052. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*