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ZENODO
Report . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Report . 2025
License: CC BY
Data sources: Datacite
ZENODO
Report . 2025
License: CC BY
Data sources: Datacite
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Australian Structural Biology Deep-Learning Infrastructure Roadmap

Authors: Michie, Katharine Arwen; Litfin, Thomas; Beecroft, Sarah Jane; Collins, Brett; Czabotar, Peter; Downton, Matthew; Doyle, Matthew Thomas; +6 Authors

Australian Structural Biology Deep-Learning Infrastructure Roadmap

Abstract

Executive Summary Enabled by advances in deep learning methods for protein structure prediction and de novo protein design, computational structural biology has rapidly emerged as a powerful technology driving innovation in both fundamental and translational science. The technology underpins breakthroughs in drug design, diagnostics, personalised medicine, and synthetic biology. However, effective use requires concentrated interdisciplinary expertise and access to modern graphics processing unit (GPU) hardware that few Australian researchers or industry can sustain. The Australian structural biology community has taken a collaborative approach to develop this infrastructure roadmap which describes the existing national landscape, identifies and prioritizes critical research bottlenecks, and proposes a national strategy to unlock the immense potential of computational structural biology for Australian researchers. This strategy is intended to evolve as the requirements of the community change. The roadmap outlines the challenges faced but also presents opportunities to maximize the value of this new technology. A robust, sovereign capability in computational structural biology and protein design will position Australian universities, research institutes, and industry at the forefront of global innovation. The community roadmap outlines 4 major deliverables including: D1. A dedicated community space to foster collaboration and share best-practice recommendations for software deployments, benchmarking, validations and insights developed within the community. D2. Community training resources to on-board diverse stakeholders within the context of computational structural biology and strengthen the national impact of community expertise. D3. National computational infrastructure built on increased hardware investment and a user platform to facilitate efficient, high-throughput utilization of national computing resources and drive translational outcomes enabled by curated and validated computational structural biology technologies. D4. Alignment, integration and engagement with global best-practice efforts for computational structural biology infrastructure and research. Achieving these outcomes will not only enable the community to add value across diverse research disciplines within Australia, but will drive innovation and enhance the national research and enterprise profile in medicine and biotechnology on the global stage.

Keywords

Deep learning, Structural biology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average