arXiv Machine Learning
Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical space
Read the original on arXiv Machine Learning →The paper introduces juniper, a discrete denoising diffusion model that performs compositional backmapping from coarse‑grained (CG) beads to detailed molecular graphs. By conditioning on the octanol–water partition free energy, the model generates valid and unique molecules that closely match the target free‑energy distribution for two‑bead systems. This approach enables the conversion of CG screening results into candidate atomistic molecules for further study or synthesis.
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