arXiv 2302.122552023-02-23q-bio.BMcs.LG

用内禀扩散模型模拟分子结构

Modeling Molecular Structures with Intrinsic Diffusion Models

University of Cambridge（剑桥大学）

Gabriele Corso

备注：: MIT Master of Science thesis. arXiv admin note: substantial text overlap with arXiv:2210.01776, arXiv:2206.01729

摘要：

自一百多年前成立以来，结构生物学领域一直致力于通过研究分子在3D空间中的结构来理解和分析分子及其相互作用的性质。然而，这种方法的一个根本挑战是这些粒子的动态性质，这迫使我们对每个分子系统的结构分布进行建模，而不是单一的。本论文提出了内在扩散模型，一种新的方法，这个问题的基础上结合扩散生成模型与科学知识的灵活性的生物复合体。这些自由度的知识被转化为定义的扩散过程的歧管的定义。该流形显著降低了维度，并增加了生成空间的平滑度，从而允许显著更快和更准确的生成过程。我们证明了这种方法在计算化学和生物学的基础上的两个基本任务：分子构象生成和分子对接的有效性。在这两项任务中，我们构建了第一个超越传统计算方法的深度学习方法，为可扩展程序实现了前所未有的准确性。

英文摘要：

Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by studying the structure that they take in 3D space. However, a fundamental challenge with this approach has been the dynamic nature of these particles, which forces us to model not a single but a whole distribution of structures for every molecular system. This thesis proposes Intrinsic Diffusion Modeling, a novel approach to this problem based on combining diffusion generative models with scientific knowledge about the flexibility of biological complexes. The knowledge of these degrees of freedom is translated into the definition of a manifold over which the diffusion process is defined. This manifold significantly reduces the dimensionality and increases the smoothness of the generation space allowing for significantly faster and more accurate generative processes. We demonstrate the effectiveness of this approach on two fundamental tasks at the basis of computational chemistry and biology: molecular conformer generation and molecular docking. In both tasks, we construct the first deep learning method to outperform traditional computational approaches achieving an unprecedented level of accuracy for scalable programs.

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。

摘要：

英文摘要：

相关论文