# Theory and Modeling of Noncovalent Binding

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $517,102

## Abstract

Project Summary
Identifying a small molecule that tightly binds a targeted protein is a time-consuming, costly step
in many drug discovery projects. Explicit solvent free energy methods can be used to predict
small molecule-protein binding affinities and thus assist with this step. However, they do not
provide consistently accurate predictions, and limitations in the force fields they use are
implicated as a key source of error. Our main goal, therefore, is to help generate more
trustworthy force fields. In particular, we aim to prove principle for the use of experimental
binding data for host-guest systems, along with traditionally used liquid properties, to refine
force field parameters. We also aim show that free energy methods can help predict ligand
binding poses and rank compound libraries against targeted proteins.
First, we will expand the chemical diversity of host-guest systems, by developing facile methods
of derivatizing cyclodextrin host molecules, and using these methods to create new, water-
soluble cyclodextrin derivatives. We will measure their binding free energies and enthalpies with
varied guest molecules, and will use these new data to test and refine force fields.
We also aim to prove principle for the use of sensitivity analysis to refine Lennard-Jones (LJ)
parameters in existing atom-typed force fields, based on host-guest binding data and liquid
property data. In addition to adjusting existing atom-typed parameters, we will develop an
atoms-in-molecules approach to mapping a quantum calculation for a molecule to LJ
parameters for that molecule. By reducing the number of parameters, relative to atom-typed
methods, this approach should enable global parameter optimization, rather than just refinement
of existing parameters.
Finally, we will automate and optimize our lab’s attach-pull-release (APR) method of computing
binding free energies so that it can be used to rank candidate poses of a ligand in a binding site;
the most stable few poses will then be used for full binding free energy calculations. Success in
this effort will enable free energy methods to be used in virtual compound screening. In
addition, we will use the APR method to test the new parameters generated above in the
context of protein-ligand binding.

## Key facts

- **NIH application ID:** 9999573
- **Project number:** 5R01GM061300-20
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** MICHAEL K. GILSON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $517,102
- **Award type:** 5
- **Project period:** 2000-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999573

## Citation

> US National Institutes of Health, RePORTER application 9999573, Theory and Modeling of Noncovalent Binding (5R01GM061300-20). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9999573. Licensed CC0.

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