# Predicting adverse drug reactions via networks of drug binding pocket similarity

> **NIH NIH F31** · STANFORD UNIVERSITY · 2024 · $42,507

## Abstract

PROJECT SUMMARY
The CDC estimates that adverse drug reactions (ADRs) cause 1.3 million emergency department visits annually
in the U.S., and that hundreds of thousands of these patients require hospitalization. ADRs are often caused by
drugs binding proteins in the body that were not intended targets. Predicting this off-target binding is difficult.
There are methods that use 3D molecular structure to predict if a small molecule can bind a given protein, but
the majority of the human proteome does not have an experimentally-solved structure. Recent breakthroughs
in protein structure prediction have enabled high confidence prediction of nearly any protein's structure from
sequence alone, meaning that we can leverage structure information for the entire human proteome in a way
that was impossible two years ago. Additionally, recent advances in structural informatics algorithms have
improved our ability to identify locations on a protein surface with high binding propensity; despite this,
current ADR prediction algorithms are unable to both leverage binding information of functionally
uncharacterized proteins and make interpretable predictions that can guide drug design. I propose to create
methods to predict drug binding pockets and ADRs in an interpretable manner at the proteome scale. I will
accomplish this by 1) building a graph representation of known and predicted drug-pocket pairs; 2) using this
graph to estimate ADRs associated with pockets and drugs; and 3) extending the pocket and ADR prediction
methods to predict and explain ADRs caused by proteome-wide off-target binding. Application of the proposed
method to the entire human proteome will allow the prediction of a drug's potential ADRs before it is used in
humans, improving drug development and reducing the number of ADRs experienced.
I will conduct this project in the lab of Dr. Russ Altman at Stanford University, where I am working toward my
long-term career goal of becoming an independent researcher developing computational methods that
accelerate drug development and aid understanding of drug response at the molecular level. My training
environment sets me up well to achieve this goal as Dr. Altman has an excellent track record of mentoring
graduate students and Stanford University provides a plethora of educational resources and a highly
collaborative research environment. The Altman group has developed algorithms for characterizing protein
microenvironments and has a history in both computational structural biology and drug response research,
providing me with easy access to experts in domains highly relevant to my proposed work. Beyond the
proposed research, my training plan includes attending seminars and conferences, collaborating with other
research groups, taking additional coursework, teaching, and oral and written communication of my work.

## Key facts

- **NIH application ID:** 10951510
- **Project number:** 5F31GM151783-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Kristy Carpenter
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $42,507
- **Award type:** 5
- **Project period:** 2023-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10951510

## Citation

> US National Institutes of Health, RePORTER application 10951510, Predicting adverse drug reactions via networks of drug binding pocket similarity (5F31GM151783-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10951510. Licensed CC0.

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