Uni-Mol3: A Multi-Molecular Foundation Model for Advancing Organic Reaction Modeling

TL;DR

Uni-Mol3 is a hierarchical deep learning framework that models multi-molecular reactions by encoding 3D molecular structures into a language, pre-training on molecular and reaction data, and fine-tuning for diverse reaction tasks, outperforming existing methods.

Contribution

Introduces Uni-Mol3, a novel multi-molecular reaction modeling framework combining hierarchical encoding, multi-stage pre-training, and prompt-aware fine-tuning for improved reaction prediction.

Findings

Outperforms existing methods on 10 datasets across 4 tasks.

Effectively models complex organic reactions with high accuracy.

Demonstrates strong generalizability in multi-task prediction.

Abstract

Organic reaction, the foundation of modern chemical industry, is crucial for new material development and drug discovery. However, deciphering reaction mechanisms and modeling multi-molecular relationships remain formidable challenges due to the complexity of molecular dynamics. While several state-of-the-art models like Uni-Mol2 have revolutionized single-molecular representation learning, their extension to multi-molecular systems, where chemical reactions inherently occur, has been underexplored. This paper introduces Uni-Mol3, a novel deep learning framework that employs a hierarchical pipeline for multi-molecular reaction modeling. At its core, Uni-Mol3 adopts a multi-scale molecular tokenizer (Mol-Tokenizer) that encodes 3D structures of molecules and other features into discrete tokens, creating a 3D-aware molecular language. The framework innovatively combines two pre-training stages: molecular pre-training to learn the molecular grammars and reaction pre-training to capture fundamental reaction principles, forming a progressive learning paradigm from single- to multi-molecular systems. With prompt-aware downstream fine-tuning, Uni-Mol3 demonstrates exceptional performance in diverse organic reaction tasks and supports multi-task prediction with strong generalizability. Experimental results across 10 datasets spanning 4 downstream tasks show that Uni-Mol3 outperforms existing methods, validating its effectiveness in modeling complex organic reactions. This work not only ushers in an alternative paradigm for multi-molecular computational modeling but also charts a course for intelligent organic reaction by bridging molecular representation with reaction mechanism understanding.