Zhang Kaiwei, Lin Yange, Wu Guangcheng, Ren Yuxiang, Zhang Xuecang, Wang Bo, Zhang Xiao-Yu, Du Weitao
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100085, China.
Huawei Technologies, Shenzhen, China.
BMC Bioinformatics. 2025 May 7;26(1):123. doi: 10.1186/s12859-025-06072-w.
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.
深度学习，尤其是人工智能生成的内容，与从头计算得出的高质量数据相结合，已成为改变科研格局的一条有前景的途径。然而，设计包含多模态先验知识的分子药物或材料仍然是一项关键且复杂的任务。具体而言，要实现切实可行的分子设计，不仅需要满足多样性要求，还需解决领域专家所概述的具有各种对称性的结构和纹理约束。在本文中，我们提出了一种创新方法，将这个逆设计问题表述为多模态引导优化任务来加以解决。我们提出的解决方案涉及一个用于执行分子优化任务的纹理 - 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形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指定的对称结构和纹理约束的分子结构。在三种引导优化设置下的实验表明，与现有最先进方法相比，其具有卓越的命中优化性能。此外，3DToMolo展示了无需先验知识就能发现包含特定目标子结构的潜在新型分子的能力。这项工作不仅对深度学习方法的发展具有普遍意义，还为分子设计策略的变革性转变铺平了道路。3DToMolo为更细致、有效地探索广阔的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的前沿领域。
