AI in Molecular Life Science

The use of Artificial Intelligence (AI) in molecular life science is a rapidly growing domain encompassing machine learning models for disease risk and biomarker discovery, deep learning tools for bioinformatics analysis, and the use of generative AI for code development and biomarker interpretation. The scale and complexity of modern biological datasets, including genomics, proteomics, spatial omics and beyond, makes life science research particularly amenable to these approaches, from AlphaFold's breakthrough in protein structure prediction to advances in drug discovery, synthetic biology, and single-cell analysis.

Join the conversation - all welcome!

Enabling Australian researchers to harness these methods requires coordinated approaches and investment in compute infrastructure, software ecosystems, and expertise.

We are providing a focal point for Australian molecular life science researchers, bioinformaticians, and developers to share insights and voice their infrastructure requirements to apply AI in molecular life science.

We are actively seeking a wide range of opinions to help us:

• Understand current and emerging AI use-cases across all life science domains.

• Identify the key challenges researchers face in using, training, or deploying AI models.

• Map the shared priorities, gaps, and dependencies for AI infrastructure (including compute, data, software, training, and expertise).

Your input is critical to informing what computational infrastructure, services, and training are needed to better support AI-driven research within Australia and globally.

During 2026 we will be drafting an “AI in Life Science Infrastructure Roadmap for Australia” which will present a vision for shared national infrastructure that will help molecular life science researchers harness AI for discovery.

Header image: Created using Freepik AI Image Generator. Setting: Flux 1.0 Fast. Prompt: “AI neural network illustrated as abstract geometry with dynamic, luminous connection pathways”.