# Computational Explorations of Unconventional Approaches to Control Noncovalent Interactions

> **NIH NIH R35** · UNIVERSITY OF HOUSTON · 2020 · $377,200

## Abstract

PROJECT SUMMARY
The research interests of my group are rooted in explorations of new and useful conceptual models to improve
the control and prediction of noncovalent interactions. Our research involves the use of a variety of
computational quantum chemical tools, applications of density functional theory (DFT), cheminformatics, and
machine-learning methods. A premise of our research is that aromaticity may be used to modulate many types
of noncovalent interactions (such as hydrogen bonding, π-stacking, anion-π interactions). The reciprocal
relationship we find, between “aromaticity” in molecules and the strengths of “noncovalent interactions,” is
surprising especially since they are typically considered as largely separate ideas in chemistry. The innovation
of this research is that it will enable use of intuitive “back-of-the-envelope” electron-counting rules (such as the
4n+2πe Hückel rule for aromaticity) to make predictions of experimental outcomes regarding the impact of
noncovalent interactions. A five-year goal is to realize the use of our conceptual models in real synthetic
examples prepared by our experimental collaborators. My research vision is to bridge discoveries of innovative
concepts to their practical impacts for biomedical and biomolecular research.

## Key facts

- **NIH application ID:** 10016376
- **Project number:** 5R35GM133548-02
- **Recipient organization:** UNIVERSITY OF HOUSTON
- **Principal Investigator:** Judy I-Chia Wu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $377,200
- **Award type:** 5
- **Project period:** 2019-09-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10016376

## Citation

> US National Institutes of Health, RePORTER application 10016376, Computational Explorations of Unconventional Approaches to Control Noncovalent Interactions (5R35GM133548-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10016376. Licensed CC0.

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