Text-guided Molecule Generation with Conditional Discrete Graph Diffusion Model

Abstract

Text-guided molecule generation enables controlled molecular design from natural language descriptions and has broad applications in areas such as drug discovery. While recent methods have demonstrated promising capability in generating molecules that align well with textual descriptions, they often overlook the structural properties of the generated graphs. As a result, these approaches struggle to simultaneously ensure consistency with the input text and high structural quality of the generated molecules. In this paper, we propose a text-guided molecular graph generation framework that leverages the structural modeling power of graph diffusion models to achieve both strong alignment with textual descriptions and high-quality molecular structures. However, accomplishing this goal involves several key challenges: 1) how to align graph diffusion models with natural language instructions in order to generate molecular graphs with expected relational semantics from text, 2) how to directly optimize the quality of the generated molecular graphs without sacrificing fine-grained alignment with text-specific details. To tackle these challenges, we introduce Text-guided Conditional Discrete Graph Diffusion (TDGD), a discrete diffusion-based framework for generating molecular graphs from natural language descriptions. Our model incorporates a structure-aware cross-attention mechanism that aligns textual semantics with molecular structures by capturing relational semantics between textual descriptions and molecular structures. In addition, we propose a molecule structure consistency loss that explicitly enforces structural coherence during generation, leading to higher-quality and more consistent molecular graphs. Extensive experiments on ChEBI-20 and L+M-24 datasets demonstrate the effectiveness of our proposed TDGD model.