# Accounting for Water Structure and Thermodynamics in Computer-Aided Drug Design

> **NIH NIH R01** · HERBERT H. LEHMAN COLLEGE · 2021 · $341,123

## Abstract

Project Summary
This project aims to develop new methods and computational tools that will speed structure-based drug-
discovery by providing a detailed analysis of hydration structure and thermodynamics in targeted protein
binding pockets, and incorporating this information into fast docking algorithms. Key aims are to extend the
evaluation of grid-based inhomogeneous solvation theory (GIST) entropy terms up to second order, develop
methods that allow GIST to be applicable to polarizable fields, and exploit empirical data on the patterning of
hydrogen bonding sites surrounding bridging water molecules to develop a Pseudo Explicit Water (PEW)
method that accounts for the thermodynamic consequences of water-mediated protein-ligand interactions.
The extended GIST and PEW methods will be integrated, individually and in combingation, into fast new
scoring functions for ligand docking and scoring, for which promising preliminary results are provided in this
proposal. Finally, in order to maximize scientific and health impact, software capable of implementing these
advances will be packaged, documented and disseminated as part of the freely available, widely used and open
source AMBER Tools and DOCK software suites.

## Key facts

- **NIH application ID:** 10167722
- **Project number:** 5R01GM100946-08
- **Recipient organization:** HERBERT H. LEHMAN COLLEGE
- **Principal Investigator:** Thomas Philip Kurtzman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $341,123
- **Award type:** 5
- **Project period:** 2013-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10167722

## Citation

> US National Institutes of Health, RePORTER application 10167722, Accounting for Water Structure and Thermodynamics in Computer-Aided Drug Design (5R01GM100946-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10167722. Licensed CC0.

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