What is it about?
Molecular simulations face a trade-off: atomistic models are precise but slow, while coarse-grained ones are faster but lose critical details. Our framework, HEroBM, uses AI to restore those missing details with high accuracy and can be applied to virtually any type of molecules — including proteins, lipids, and small organic compounds. This flexibility makes it possible to backmap entire multi-component systems, such as drug–protein complexes embedded in membranes, into stable atomistic structures ready for further study.
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Why is it important?
HEroBM makes it possible to combine the speed of coarse-grained simulations with the accuracy of atomistic models. This bridges a long-standing gap in computational biophysics and drug discovery, where researchers often had to choose one at the expense of the other. By enabling reliable transitions between scales, HEroBM provides a powerful new tool to study biomolecules in realistic environments and to generate atom-level insights that are crucial for designing effective drugs.
Perspectives
Beyond just solving the backmapping problem, I hope HEroBM serves as a practical example of how AI can be genuinely useful in molecular biology. What's most interesting to me is seeing AI tools like this integrate directly into existing simulation workflows. The idea is to help coarse-grained modeling evolve from a useful simplification into a more robust, AI-enhanced framework. Ultimately, the goal is to make these advanced tools applicable to real-world research. If my work can contribute, even in a small way, to speeding up the design of new medicines or helping scientists investigate more complex biological systems, I would consider it a success.
Daniele Angioletti
Universita della Svizzera Italiana
Read the Original
This page is a summary of: HEroBM: A deep equivariant graph neural network for high-fidelity backmapping from coarse-grained to all-atom structures, The Journal of Chemical Physics, August 2025, American Institute of Physics,
DOI: 10.1063/5.0280330.
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