Catalysts for Change in Digital Chemistry: Predictive Discovery and Supercomputing Synergy
Year: 2025
Type: article
Abstract: Over the last 25 years, Digital Chemistry has evolved from a supporting function to a driving force in drug discovery. My journey has been at the forefront of this transformation, leveraging supercomputing to automate molecular design, drastically reduce the number of physical experiments, and accelerate the path to clinical candidates. In this keynote, I will explore how predictive modelling - from classical QSAR to physics-based simulations, quantum computing, and modern large language models - is not just enabling faster discoveries but redefining what is possible. I will illustrate how the hybridisation of these computational techniques is creating scalable, sustainable workflows that deliver profound scientific and environmental efficiencies. Most importantly, I will discuss how collaboration and leadership in the supercomputing community can amplify these advancements, catalysing the next generation of change.
Author: Brown, Nathan
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7103750730
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