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 (this https URL).

通过分析得分模型，本研究发现生成分子过程中的得分模型在起始时类似恢复势能，在最后则类似量子力学力，两者之间表现出特殊性质以便生成较大的分子。基于训练出的模型，本研究提出了一种新的零样本分子生成方法 —— 基于相似性的分子生成（SiMGen），该方法结合了时间相关的相似性核和预训练的机器学习力场描述符，能够实现不进行进一步训练的分子生成，并通过点云先验实现对分子形状的完全控制，并支持条件生成。研究者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。