Design macromolecular interactions by in-painting full-atom models.
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AtomPaint
AtomPaint is a collection of convolutional neural networks (CNNs) meant for learning from 3D images of macromolecular structures. A unique, unifying feature of many of these networks is that maintain rotational equivariance; that is, they will recognize features no matter what orientation they appear in. The models are implemented using the PyTorch and ESCNN frameworks.
Below are links to some of the datasets that these models can be trained on:
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