Mol-CADiff: Causality-Aware Autoregressive Diffusion for Molecule Generation
This work addresses the need for more intuitive language-driven molecular design in drug discovery and materials science, representing a novel method for a known bottleneck.
The paper tackles the problem of generating molecules based on textual descriptions, a challenge in drug discovery and materials science, and introduces Mol-CADiff, a diffusion-based framework that uses causal attention mechanisms to improve alignment and outperforms state-of-the-art methods in generating diverse, novel, and chemically valid molecules.
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.