  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 various
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.
