  Computationally generating novel synthetically accessible compounds with high
affinity and low toxicity is a great challenge in drug design. Machine-learning
models beyond conventional pharmacophoric methods have shown promise in
generating novel small molecule compounds, but require significant tuning for a
specific protein target. Here, we introduce a method called selective iterative
latent variable refinement (SILVR) for conditioning an existing diffusion-based
equivariant generative model without retraining. The model allows the
generation of new molecules that fit into a binding site of a protein based on
fragment hits. We use the SARS-CoV-2 Main protease fragments from Diamond
X-Chem that form part of the COVID Moonshot project as a reference dataset for
conditioning the molecule generation. The SILVR rate controls the extent of
conditioning and we show that moderate SILVR rates make it possible to generate
new molecules of similar shape to the original fragments, meaning that the new
molecules fit the binding site without knowledge of the protein. We can also
merge up to 3 fragments into a new molecule without affecting the quality of
molecules generated by the underlying generative model. Our method is
generalizable to any protein target with known fragments and any
diffusion-based model for molecule generation.
