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.
翻译:AI-基础分子生成为大量生物医学科学和工程学领域,如抗体设计、水解系统工程或疫苗开发提供了很有希望的方法。由于分子受物理定律的制约,关键的挑战是如何将事先信息纳入培训程序,以产生高质量和现实的分子。我们提出了一个简单而新颖的方法,用物理和统计信息来指导基于传播的基因模型的培训。这通过建造物理上知情的传播桥梁、保证在固定终端时间得出特定观测结果的随机过程来实现。我们开发了基于Lyapunov功能的建造和确定桥梁的方法,并提出了若干关于高质量分子生成和统一推动的3D点云生成的先行信息桥梁的建议。我们进行全面实验,表明我们的方法为3D一代的任务提供了强有力的方法,产生了质量和稳定性更佳的分子结构以及质量更高和稳定性更一致分布高品质的点云。