David Hargreaves
Papers
1
Total Citations
3
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1
About
David Hargreaves is a researcher at the intersection of artificial intelligence and structural biology, with a primary focus on applying deep learning to macromolecular crystallography. His most notable contribution is the development of practical, accessible AI methods for automating the classification of crystallisation experiment images—a traditionally labor-intensive bottleneck in protein structure determination. In his highly cited 2022 paper, Hargreaves demonstrated that with a relatively modest training set of just 16,000 images, standard convolutional neural network architectures can reliably identify promising crystallisation conditions, making deep learning tools more attainable for routine laboratory use. This work has earned 3 citations and is recognized for its pragmatic approach to bridging the gap between cutting-edge AI and everyday bench science. By showing that sophisticated image classification does not require massive datasets or custom-built networks, Hargreaves has helped democratise automation in structural biology, enabling faster screening and higher throughput in crystallisation trials. His research continues to explore how accessible machine learning can transform experimental workflows in the life sciences.
Research Focus
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Top Papers
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