# Novel computational approaches to predict drug response and combination effects

> **NIH NIH R35** · UT SOUTHWESTERN MEDICAL CENTER · 2021 · $409,479

## Abstract

Summary
Tailoring the most desired therapy to each individual patient is the primary goal of precision medicine. A
reliable and robust predictive model of drug effectiveness based on patients' unique genomic background is
the key. For decades, communities have been trying to establish the relationship between molecular
characteristics and drug response in complex diseases. Over the last decade, a large amount of genomic and
epigenomic data together with pharmacogenomics data and response to perturbations data has been
generated for many human cell lines through collaborations in the research community. These projects have
led to significant therapeutic discoveries and have provided unprecedented opportunities to predict drug
response using molecular fingerprints. However, even with great interest and effort in developing
computational methods for predicting drug response, the prediction accuracies are at best only moderate. A
related but distinct question is to understand the mechanisms of action (MOA) of drugs. Understanding drug
MOAs enables characterization of drug side effects and identification of old drugs for new uses (i.e. drug
repositioning). The traditional experimental assays to identify MOAs of drugs are expensive and time-
consuming. There are three key questions to be addressed in the study. 1. Can novel computational
approaches largely improve prediction accuracy of response to single drugs using comprehensive genomic
and chemical information? 2. Can computational approaches provide a systematic way to mine genomics and
drug response data to generate biological insights into the mechanisms of actions of various drugs? 3. Is it
possible to develop an interpretable and accurate computation model to predict drug combination effects using
pharmacogenomics data? Inherent features make it very challenging to predict drug response accurately:
High-dimensionality of input data, the complex relationship between input features and response data; and
heterogeneous drug/compound response patterns across different genetic lineages. Recently, artificial
intelligence (AI) has been making remarkable strides in various applications owing to the rapid progress of
“deep learning. In Aim 1 of this study, we will develop novel AI-based approaches to address the
computational challenges of improving the prediction accuracy of drug response. In Aim 2 of the study, we will
develop a novel computation framework to study of MOA of drugs. In Aim 3, we will develop an interpretable
deep-learning based computational framework to predict drug combination effects. In addition, we will develop
a user-friendly web portal as an integrated research platform to share the methodology, algorithms and data
generated from this proposed study to the research community.

## Key facts

- **NIH application ID:** 10133094
- **Project number:** 5R35GM136375-02
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Yang Xie
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $409,479
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10133094

## Citation

> US National Institutes of Health, RePORTER application 10133094, Novel computational approaches to predict drug response and combination effects (5R35GM136375-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10133094. Licensed CC0.

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