  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).
