基于相似性核的零样本分子生成
Zero Shot Molecular Generation via Similarity Kernels
AI总结：
本文通过分析基于能量的扩散模型中分数的行为，提出零样本分子生成方法SiMGen，利用时间相关相似性核和预训练力场描述符实现无需训练的可控分子生成。
摘要：
生成建模旨在通过直接提出具有理想性质的结构来加速新型化学物质的发现。近年来，基于分数的生成模型（即扩散生成模型）已显著优于先前的方法。其成功的关键在于分数与物理力之间的密切关系，这使得可以使用强大的等变神经网络。然而，目前对学习到的分数的行为尚未得到充分理解。在此，我们通过训练一个基于能量的扩散模型用于分子生成来分析该分数。我们发现，在生成过程中，分数在初始阶段类似于恢复势，而在结束时则类似于量子力学力。在两个端点之间，分数表现出特殊的性质，从而能够构建大分子。利用从训练模型中获得的见解，我们提出了基于相似性的分子生成（SiMGen），这是一种用于零样本分子生成的新方法。SiMGen将时间相关的相似性核与来自预训练机器学习力场的描述符相结合，无需任何进一步训练即可生成分子。我们的方法允许通过点云先验对分子形状进行完全控制，并支持条件生成。我们还发布了一个交互式网络工具，使用户能够在线使用SiMGen生成结构（https://zndraw.icp.uni-stuttgart.de）。
英文摘要：
Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches. Key to their success is the close relationship between the score and physical force, allowing the use of powerful equivariant neural networks. However, the behaviour of the learnt score is not yet well understood. Here, we analyse the score by training an energy-based diffusion model for molecular generation. We find that during the generation the score resembles a restorative potential initially and a quantum-mechanical force at the end. In between the two endpoints, it exhibits special properties that enable the building of large molecules. Using insights from the trained model, we present Similarity-based Molecular Generation (SiMGen), a new method for zero shot molecular generation. SiMGen combines a time-dependent similarity kernel with descriptors from a pretrained machine learning force field to generate molecules without any further training. Our approach allows full control over the molecular shape through point cloud priors and supports conditional generation. We also release an interactive web tool that allows users to generate structures with SiMGen online (https://zndraw.icp.uni-stuttgart.de).
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。
