# Using Common Fund datasets for xenobiotic localization

> **NIH NIH R03** · UNIVERSITY OF TEXAS EL PASO · 2021 · $304,000

## Abstract

Project Summary
Subcellular localization, such as the nucleus lysosomes, and mitochondria, has tremendous potential to
enhance the effectiveness of the therapeutic molecules rather than random distribution throughout the cell.
With improved subcellular localization and enhanced concentration, a specific molecule can be more
efficacious as well as less toxic which is usually a concern of random distribution and nonspecific localization.
Therefore, understanding subcellular distribution and the mechanism for a specific molecule can further
modulate subcellular dysfunction mediated diseases. Xenobiotic localization at the subcellular level has a
profound effect on several processes. The overarching goal of the proposed work is to develop a novel
platform with computational tools for specific xenobiotic localization. The proposed work will take advantage of
three common fund datasets. In specific aim-1, we aim to develop a suite of machine learning (ML) models for
hierarchical levels of micro-compartmentation and 40 specific subcellular locations. These machine learning
models will be first built using three different types of features (fingerprints-based, pharmacophore-based, and
physicochemical descriptors-based). Then, they are fused using an advanced multilayer combinatorial fusion
algorithm to get the best consensus model. We will also perform the scaffold analysis to identify critical
scaffolds that play a role in accumulating molecules at specific subcellular locations. In specific aim-2, we will
conduct experimental validation of the predictions developed ML models. More specifically we will test 50
compounds for their subcellular location. In specific aim-3, we plan to build an open portal that incorporates
datasets, ML model, prediction server, and documentation. All the data and models generated from the project
are made available as open-source.

## Key facts

- **NIH application ID:** 10357502
- **Project number:** 1R03OD032624-01
- **Recipient organization:** UNIVERSITY OF TEXAS EL PASO
- **Principal Investigator:** Md Nurunnabi
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $304,000
- **Award type:** 1
- **Project period:** 2021-09-22 → 2023-09-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10357502

## Citation

> US National Institutes of Health, RePORTER application 10357502, Using Common Fund datasets for xenobiotic localization (1R03OD032624-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10357502. Licensed CC0.

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