具有信息先验桥的基于扩散的分子生成
Diffusion-based Molecule Generation with Informative Prior Bridges
摘要：
基于AI的分子生成为生物医学科学和工程的大领域提供了一种有前途的方法，例如抗体设计，水解酶工程或疫苗开发。由于分子受物理定律支配，因此一个关键的挑战是将先验信息纳入训练过程以生成高质量和真实的分子。我们提出了一种简单而新颖的方法来指导基于物理和统计先验信息的扩散生成模型的训练。这是通过构建物理上知情的扩散桥，随机过程，保证在固定的终端时间产生一个给定的观察。我们开发了一种基于李雅普诺夫函数的方法来构建和确定桥梁，并提出了一些建议，信息先验桥梁高质量的分子生成和均匀性促进的3D点云生成。通过全面的实验，我们证明了我们的方法为3D生成任务提供了一种强大的方法，产生了具有更好质量和稳定性分数以及更均匀分布的高质量点云的分子结构。
英文摘要：
AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine development. Because the molecules are governed by physical laws, a key challenge is to incorporate prior information into the training procedure to generate high-quality and realistic molecules. We propose a simple and novel approach to steer the training of diffusion-based generative models with physical and statistics prior information. This is achieved by constructing physically informed diffusion bridges, stochastic processes that guarantee to yield a given observation at the fixed terminal time. We develop a Lyapunov function based method to construct and determine bridges, and propose a number of proposals of informative prior bridges for both high-quality molecule generation and uniformity-promoted 3D point cloud generation. With comprehensive experiments, we show that our method provides a powerful approach to the 3D generation task, yielding molecule structures with better quality and stability scores and more uniformly distributed point clouds of high qualities.
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。
