# Machine learning approaches to predict Acetylcholinesterase inhibition and model applications

> **NIH NIH R44** · COLLABORATIONS PHARMACEUTICALS, INC. · 2024 · $727,312

## Abstract

Summary
Organophosphorus (OP) compounds are one of the most common causes of poisoning worldwide. There are
nearly 3 million poisonings per year resulting in three hundred thousand deaths. OPs bind to acetylcholinesterase
(AChE), rendering the enzyme incapable of hydrolyzing acetylcholine (ACh) in the cholinergic synapses and
neuromuscular junctions. Subsequent accumulation of ACh leads to overstimulation of the affected neurons
acting through muscarinic and nicotinic receptors. Some of the adverse effects of pesticides on non-target
organisms such as fish, amphibians and humans have also occurred as a result of biomagnifications of the toxic
compounds. Since AChE is a very well-studied target, the likelihood of finding further new inhibitors amongst
well-known drugs and molecules is likely unlikely and therefore we need to look further afield and do this in a
more efficient manner. Computational approaches such as machine learning are an approach that can be used
to learn from diverse literature to enable virtual screening. Following our preliminary work generating Bayesian
models for human AChE we have performed a high throughput screen of 2431 compounds and identified 66
compounds that were “active” against eel AChE. We recently completed a phase I SBIR in which we expanded
the use of these machine learning models to curate additional AChE data, built and validated AChE models with
many algorithms in parallel and used them to identify AChE inhibitors from various species to predict OP induced
poisoning and possible threats to the environment. In addition, we have recently curated further literature data
and generated models for the closely related enzyme BChE using a contrastive learning algorithm approach to
score a library of 5 million molecules from various vendors in order to select molecules for testing and have
identified several novel selective inhibitors. In Phase II we will develop the MegaAChE software product further
for prediction of AChE inhibition and incorporate uncertainly prediction features and new algorithms such as
large language models. We will also use this software to design and develop AChE reactivators that could
provide a new treatment for OP pesticide and nerve agent exposure. We now propose the following aims to
further develop and validate the MegaAChE software as a commercial product:
Aim 1: Modeling uncertainty in AChE models
Aim 2: Large language models for AChE and BChE datasets
Aim 3: Develop new reactivators to treat AChE poisoning using generative design
Our ultimate goal will be to provide software and models to predict AChE inhibition which will be a commercial
product. In addition we will develop new molecule intellectual property which we can then license to a larger
company. Computational models for AChE therefore have beneficial dual-use potential both for identifying
molecules that could have environmental or human toxicity, while alternatively such machine learning models
could also help us identify ...

## Key facts

- **NIH application ID:** 11004542
- **Project number:** 2R44ES033855-02
- **Recipient organization:** COLLABORATIONS PHARMACEUTICALS, INC.
- **Principal Investigator:** SEAN EKINS
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $727,312
- **Award type:** 2
- **Project period:** 2021-12-10 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11004542

## Citation

> US National Institutes of Health, RePORTER application 11004542, Machine learning approaches to predict Acetylcholinesterase inhibition and model applications (2R44ES033855-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11004542. Licensed CC0.

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