# Machine Learning for Drug Response Prediction

> **NIH NIH R35** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $331,020

## Abstract

PROJECT SUMMARY/ABSTRACT
 Finding new drugs in the seas of small molecules is a difficult task if no prior information is available. Our
broad research goal is to develop innovative and accurate machine learning algorithms to predict the drug
responses related to complex human diseases. Specifically, we pursue questions of how a cell line responds to a
single drug and combinatorial therapies, from the perspective of biological networks and small-molecule
chemoinformatics. One research goal is to understand and predict the cell line-specific responses through
integrating a wide range of methods, including the propagation of drug effects via biological networks, matrix
factorization of molecular profiles and chemoinformatic analysis of small molecules. We will deploy our
algorithms to softwares and web servers, which will inform the downstream experimental design to identify the
single and combinatorial drug candidates against human diseases. Our research program will contribute to
accelerate the drug discovery process by in silico screening through large amount of potent chemicals.

## Key facts

- **NIH application ID:** 10005370
- **Project number:** 5R35GM133346-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Yuanfang Guan
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $331,020
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005370

## Citation

> US National Institutes of Health, RePORTER application 10005370, Machine Learning for Drug Response Prediction (5R35GM133346-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10005370. Licensed CC0.

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