UniIF: Unified Molecule Inverse Folding

TL;DR

UniIF is a unified model for molecule inverse folding that leverages a common data representation and a geometric attention network, outperforming existing methods across proteins, RNA, and materials.

Contribution

We introduce UniIF, a unified framework combining data and model innovations to handle inverse folding for all molecules, advancing beyond specialized prior approaches.

Findings

Outperforms state-of-the-art methods in protein, RNA, and material design tasks.

Uses a unified block graph data form for all molecules.

Employs a geometric block attention network capturing 3D interactions.

Abstract

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.