  Many processes in biology and drug discovery involve various 3D interactions
between molecules, such as protein and protein, protein and small molecule,
etc. Given that different molecules are usually represented in different
granularity, existing methods usually encode each type of molecules
independently with different models, leaving it defective to learn the
universal underlying interaction physics. In this paper, we first propose to
universally represent an arbitrary 3D complex as a geometric graph of sets,
shedding light on encoding all types of molecules with one model. We then
propose a Generalist Equivariant Transformer (GET) to effectively capture both
domain-specific hierarchies and domain-agnostic interaction physics. To be
specific, GET consists of a bilevel attention module, a feed-forward module and
a layer normalization module, where each module is E(3) equivariant and
specialized for handling sets of variable sizes. Notably, in contrast to
conventional pooling-based hierarchical models, our GET is able to retain
fine-grained information of all levels. Extensive experiments on the
interactions between proteins, small molecules and RNA/DNAs verify the
effectiveness and generalization capability of our proposed method across
different domains.

Machine Learning;Biomolecules