Mol-CADiff: Causality-Aware Autoregressive Diffusion for Molecule Generation

• URL: http://arxiv.org/abs/2503.05499v1
• Date: Fri, 07 Mar 2025 15:10:37 GMT
• Title: Mol-CADiff: Causality-Aware Autoregressive Diffusion for Molecule Generation
• Authors: Md Atik Ahamed, Qiang Ye, Qiang Cheng,
• Abstract summary: Mol-CADiff is a novel diffusion-based framework that uses causal attention mechanisms for text-conditional molecular generation.<n>Our approach explicitly models the causal relationship between textual prompts and molecular structures, overcoming key limitations in existing methods.<n>Our experiments demonstrate that Mol-CADiff outperforms state-of-the-art methods in generating diverse, novel, and chemically valid molecules.
• Score: 13.401822039640297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The design of novel molecules with desired properties is a key challenge in drug discovery and materials science. Traditional methods rely on trial-and-error, while recent deep learning approaches have accelerated molecular generation. However, existing models struggle with generating molecules based on specific textual descriptions. We introduce Mol-CADiff, a novel diffusion-based framework that uses causal attention mechanisms for text-conditional molecular generation. Our approach explicitly models the causal relationship between textual prompts and molecular structures, overcoming key limitations in existing methods. We enhance dependency modeling both within and across modalities, enabling precise control over the generation process. Our extensive experiments demonstrate that Mol-CADiff outperforms state-of-the-art methods in generating diverse, novel, and chemically valid molecules, with better alignment to specified properties, enabling more intuitive language-driven molecular design.

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