在文本-3D空间中塑造分子：一种用于面向文本的分子优化的灵活子结构感知框架
Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
AI总结：
本文提出3DToMolo文本-结构对齐对称扩散框架，将多模态分子逆向设计转化为引导优化任务，可在无先验知识时生成含指定子结构的新分子，并在三类设置中优于现有方法。
摘要：
深度学习（尤其是AI生成内容）与源自从头算的高质量数据的融合，已成为改变科学研究格局的一条有前景的途径。然而，设计融合多模态先验知识的分子药物或材料，仍是一项关键而复杂的任务。具体而言，实现实用的分子设计不仅需要满足多样性要求，还必须处理领域专家所提出的具有各种对称性的结构和文本约束。本文中，我们提出一种创新方法来解决这一逆向设计问题，将其表述为多模态引导优化任务。我们提出的解决方案包含一个用于执行分子优化任务的文本-结构对齐对称扩散框架，即3DToMolo。3DToMolo旨在协调包括文本描述特征和图结构特征在内的多种模态，将它们无缝对齐，以生成符合该领域专家所指定的对称结构和文本约束的分子结构。在三种引导优化设置下进行的实验表明，与最先进方法相比，其具有更优异的命中优化性能。此外，3DToMolo展示了在无需先验知识的情况下发现潜在新型分子并纳入指定目标子结构的能力。本工作不仅对深度学习方法的发展具有普遍意义，也为分子设计策略的变革性转变铺平了道路。3DToMolo为更细致、更有效地探索广阔化学空间创造了机会，为开发具有定制性质和功能的分子实体开辟了新前沿。
英文摘要：
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 optimization task. Our proposed solution involves a textural-structure alignment symmetric diffusion framework for the implementation of molecular optimization tasks, namely 3DToMolo. 3DToMolo aims to harmonize diverse modalities including textual description features and graph structural features, 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 optimization settings have shown a superior hit optimization performance compared to state-of-the-art methodologies. Moreover, 3DToMolo demonstrates the capability to discover potential 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
