  Molecule inverse folding has been a long-standing challenge in chemistry and
biology, with the potential to revolutionize drug discovery and material
science. Despite specified models have been proposed for different small- or
macro-molecules, few have attempted to unify the learning process, resulting in
redundant efforts. Complementary to recent advancements in molecular structure
prediction, such as RoseTTAFold All-Atom and AlphaFold3, we propose the unified
model UniIF for the inverse folding of all molecules. We do such unification in
two levels: 1) Data-Level: We propose a unified block graph data form for all
molecules, including the local frame building and geometric feature
initialization. 2) Model-Level: We introduce a geometric block attention
network, comprising a geometric interaction, interactive attention and virtual
long-term dependency modules, to capture the 3D interactions of all molecules.
Through comprehensive evaluations across various tasks such as protein design,
RNA design, and material design, we demonstrate that our proposed method
surpasses state-of-the-art methods on all tasks. UniIF offers a versatile and
effective solution for general molecule inverse folding.
