  The integration of deep learning, particularly AI-Generated Content, with
high-quality data derived from ab initio calculations has emerged as a
promising avenue for transforming the landscape of scientific research.
However, the challenge of designing molecular drugs or materials that
incorporate multi-modality prior knowledge remains a critical and complex
undertaking. Specifically, achieving a practical molecular design necessitates
not only meeting the diversity requirements but also addressing structural and
textural constraints with various symmetries outlined by domain experts. In
this article, we present an innovative approach to tackle this inverse design
problem by formulating it as a multi-modality guidance generation/optimization
task. Our proposed solution involves a textural-structure alignment symmetric
diffusion framework for the implementation of molecular generation/optimization
tasks, namely 3DToMolo. 3DToMolo aims to harmonize diverse modalities, aligning
them seamlessly to produce molecular structures adhere to specified symmetric
structural and textural constraints by experts in the field. Experimental
trials across three guidance generation settings have shown a superior hit
generation performance compared to state-of-the-art methodologies. Moreover,
3DToMolo demonstrates the capability to generate novel molecules, incorporating
specified target substructures, without the need for prior knowledge. This work
not only holds general significance for the advancement of deep learning
methodologies but also paves the way for a transformative shift in molecular
design strategies. 3DToMolo creates opportunities for a more nuanced and
effective exploration of the vast chemical space, opening new frontiers in the
development of molecular entities with tailored properties and functionalities.
