arXiv Machine Learning By Wenyin Zhou, Hossein Azizpour

Curriculum Learning for Generative Flexible Docking

Read the original on arXiv Machine Learning →

The paper introduces a curriculum learning strategy for generative flexible docking, a task that predicts protein‑ligand complex configurations from an apo protein structure and a ligand SMILES string. By gradually incorporating examples with large conformational changes during training, the authors enhance an existing generative model, achieving a 46.52% top‑1 success rate on the PDBBind ESMFold apo benchmark and outperforming prior baselines. The approach leverages diffusion and flow‑based generative models to model the posterior distribution over ligand poses and protein conformations conditioned on the apo input.

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