# Machine learning approaches to predict Acetylcholinesterase inhibition

> **NIH NIH R43** · COLLABORATIONS PHARMACEUTICALS, INC. · 2022 · $256,385

## Abstract

Summary
Acetylcholine (Ach) is a neurotransmitter at neuromuscular junctions and synapses in the autonomic and central
nervous systems. It also functions as a signaling molecule in non-neuronal contexts related to cellular functions,
such as proliferation and differentiation, as well as performing organ functions, like wound healing in skin or
mucus production in lungs. Organophosphorus (OP) are one of the most common causes of poisoning
worldwide. There are nearly 3 million poisonings per year resulting in three hundred thousand deaths of these
approximately 8000 are in the USA. Because of their unique chemical properties, OPs bind to
acetylcholinesterase (AChE), rendering the enzyme incapable of hydrolyzing 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. The peripheral effects of excess systemic ACh include
observable toxic signs (e.g., miosis, lacrimation, salivation, fasciculation, tremors and convulsions), as well as
life- threatening cardiovascular and respiratory distress. Simultaneous progression of the cholinergic crisis within
the central nervous system ultimately induces a state of unremitting seizure known as status epilepticus.
Unmitigated OP-induced SE is associated with wide- spread neuronal damage, and concomitant cognitive and
behavioral deficits. Besides the effects directly in humans, OPs can reach humans indirectly via expose to
various types of organisms that have themselves been contaminated in the environment. 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. What is missing across public “Structure Activity/toxicity
Relationship” databases are accessible machine learning models for scientists to use to extract knowledge from
the small molecule data that is accumulating. We would propose predicting AChE inhibition from structure of the
molecule alone. Our mission is therefore to make the various public datasets much more readily accessible to
machine learning modeling by providing the underlying datasets ready to model as well as apply prebuilt models
of our own. This project therefore covers automated curation, data integration and will build a research pipeline
for machine learning model development for AChE inhibition. We now propose auto-curation of public AChE
databases which use predominantly small molecule / biological activity data (such as IC50, Ki, EC50, or % inhibition
etc), sorted by target and species. We will develop software to autocurate data, build machine learning models
and identify potential molecules that inhibit AChE from human and other species in order to predict poisoning
and possible environmental contamination. We will also validate these models with literature data outside of the
training sets and understand the applicability dom...

## Key facts

- **NIH application ID:** 10378934
- **Project number:** 1R43ES033855-01
- **Recipient organization:** COLLABORATIONS PHARMACEUTICALS, INC.
- **Principal Investigator:** SEAN EKINS
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $256,385
- **Award type:** 1
- **Project period:** 2021-12-10 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10378934

## Citation

> US National Institutes of Health, RePORTER application 10378934, Machine learning approaches to predict Acetylcholinesterase inhibition (1R43ES033855-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10378934. Licensed CC0.

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