# MegaTox for analyzing and visualizing data across different screening systems

> **NIH NIH R43** · COLLABORATIONS PHARMACEUTICALS, INC. · 2020 · $124,915

## Abstract

Project Summary
Computational toxicology aims to use rules, models and algorithms based on prior data for specific endpoints,
to enable the prediction of whether a new molecule will possess similar liabilities or not. Our recent efforts have
used sources like PubChem and ChEMBL to build predictive models for different toxicity-related and drug
discovery endpoints. Our Phase I SBIR proposal called MegaTox will provide toxicity machine learning models
developed with different algorithms for 40-50 in vitro and in vivo toxicity datasets. We propose using this
technology to generate machine learning models for predicting potential compounds against either TGF- a
target for countering chlorine induced lung inflammation as well as the adenosine A1 receptor to identify agonists
as potential anticonvulsants. In addition, we can also compile molecules that can reactivate acetylcholinesterase
which would enable the potential to discover medical countermeasures to address nerve agent and pesticide
poisoning. We will access multiple machine learning approaches and validate these Bayesian or other machine
learning models (including Linear Logistic Regression, AdaBoost Decision Tree, Random Forest, Support Vector
Machine and deep neural networks (DNN) of varying depth) with our own in-house technology for these selected
targets. We will aim for ROC values greater than 0.75 and MCC and F1 scores that are acceptable (>0.3). These
models will be used to virtually screen FDA approved drugs, clinical candidates, commercially available drugs
or other molecules. We will select up to 50 molecules to be tested using in vitro assays alongside controls for
each target. These combined efforts should in the first instance provide commercially viable treatments which
will be used to experimentally validate our computational models that can be shared with the medical
countermeasures scientific community. In summary, we are proposing to build and validate models for targets
based on public databases, select compounds for testing, create proprietary data and use this as a starting point
for further optimization of compounds if needed. Our goal is to identify at least one promising compound for each
target that we then pursue and protect our IP. We will pursue additional grant funding to take these medical
countermeasures through additional in vitro and in vivo preclinical studies. Ultimately, we will license our products
to larger companies for development prior to clinical trials.

## Key facts

- **NIH application ID:** 10094026
- **Project number:** 3R43ES031038-01S1
- **Recipient organization:** COLLABORATIONS PHARMACEUTICALS, INC.
- **Principal Investigator:** SEAN EKINS
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $124,915
- **Award type:** 3
- **Project period:** 2020-08-05 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10094026

## Citation

> US National Institutes of Health, RePORTER application 10094026, MegaTox for analyzing and visualizing data across different screening systems (3R43ES031038-01S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10094026. Licensed CC0.

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