# An Integrative Approach to Drug Repositioning Using Decision Tree Based Machine Learning

> **NIH NIH F31** · WEILL MEDICAL COLL OF CORNELL UNIV · 2021 · $46,036

## Abstract

PROJECT SUMMARY/ABSTRACT
Despite recent advances in life sciences and technology, the amount of money spent developing a
single drug has stayed drastically expensive. Overall efficiencies have caused drug development to
stay the same, with an average cost of $2.6 billion and 15 years to develop a single drug. Considering
these challenges, there is an increased need for drug repositioning, in which new indications are
found for existing or unapproved drugs. Here we introduce an approach that integrates only drug
similarity metrics, such as side effect, structure, and target similarities, to identify novel indications for
drugs. By focusing on drug similarity metrics, our proposed method allows for applications towards
orphan molecules that presently have no primary indication. To improve upon the current methods of
drug repurposing, we propose the developing of a computational approach that utilizes multiple data
types within a machine-learning framework in order to predict indications a drug may treat. Based on
the observations that similar drugs are used for similar indications, this method utilizes publicly
available databases to identify associations between drugs, and integrates drug similarity data, as
well as drug-target specific information, into a machine-learning framework in order to accurately
predict indications for these drugs. Altogether, our method provides a novel, broadly applicable
strategy that can identify novel indications, allowing for an accelerated and more efficient method for
future drug development and repositioning efforts.

## Key facts

- **NIH application ID:** 10112306
- **Project number:** 5F31LM013058-03
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Jamal Elkhader
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $46,036
- **Award type:** 5
- **Project period:** 2019-03-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10112306

## Citation

> US National Institutes of Health, RePORTER application 10112306, An Integrative Approach to Drug Repositioning Using Decision Tree Based Machine Learning (5F31LM013058-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10112306. Licensed CC0.

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