Prakhar Dutta, Jiacheng Huang, Nazli Demirpehlivan, Thomas Catley, Sylvia Whittle, Carlo Manzo, Rahul Nagshi, Rachel Owen, Giovanni Volpe
Date: 11th March 2026
Time: 18:00 – 20:00
Place: Aula Medica, Karolinska Institute, Solna
Conference Protein Folding in Real Time, 11-13 March 2026, Stockholm, Sweden
Abstract: Atomic force microscopy (AFM) resolves biological structure and mechanics at high resolution, but produces vast, heterogeneous datasets that are often noisy and very time-consuming to analyse. Although deep learning could automate quality control, segmentation and feature extraction, adoption is limited by scarce ground-truth training data and high technical barriers for experimentalists. Here we present ASAP, an open-source tutorial and pipeline implemented in DeepTrack to provide a reproducible foundation for AI-enabled AFM. At the protein folding conference, a dual-pathway simulation for DNA, offering both molecular dynamics and rapid, non-MD geometries to generate perfect ground truth for segmentation training was presented. By consolidating simulation and learning into a single modular ecosystem, this work enables users to build upon our pipeline to optimize AFM workflows for more efficient data acquisition and robust processing.