AI-Optimised mRNA Therapies to Combat Childhood Dementia
La Trobe University
The Challenge
5 millions of Australians are directly affected by neurological and psychiatric conditions. The targeted rare neurodegenerative disorder (Niemann-Pick type C1, colloquially, “Childhood Alzheimer’s”) affects about 1 in 100,000 live births globally. Next-generation mRNA gene therapies are emerging as an effective prevention and treatment method for childhood dementia.
However, the complexity of biological data coupled with the difficulty of translating in-silico predictions into reliable in-vitro or in-vivo outcomes remain central obstacles to progress in mRNA therapies.
The Partnership
The Florey Institute of Neuroscience and Mental Health(The Florey) is collaborating with Cisco Research Chair of AI and IoT Professor Wei Xiang and the Australian Centre for AI in Medical Innovation (ACAMI) on using AI to optimise mRNA sequences for childhood dementia therapies.
This collaboration not only leverages ACAMI’s expertise in advanced AI architectures and data-driven modeling but also The Florey’s strong capabilities in cellular experimentation.
The Solution
The project is seeing the development of an advanced AI model based on biologically-informed neural networks and transformers (the technology behind ChatGPT and other large language models).
Building on recent breakthroughs in natural language processing and biological sequence modeling, the project is progressively refining training strategies to capture the subtle relationships between nucleotide composition, structural stability, and translational efficiency.
Recognition & Impact
Project Highlights
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10¹⁰⁰–¹⁰⁰⁰ mRNAs as a vast combinatorial space, exhaustive testing is impossible. Advanced AI models will eliminate the need to go through every possible candidate.
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Strong results demonstrate promising capabilities in predicting mRNAs’ half-lives, a major biophysical quantity related to in-cell mRNA stability.
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By integrating computational prediction with in vitro validation, the project creates a powerful feedback loop that accelerates discovery, refines model accuracy, and drives meaningful advances in mRNA-based gene therapy for childhood dementia.
Contact Person & Details
- Prof Wei Xiang, Distinguished Professor, NIIN Research Chair in AI and IoT, School of Computing, Engineering & Mathematical Sciences, La Trobe University
- Jeff Jones, Director, Innovation Central Melbourne jeffrey.jones@latrobe.edu.au