  The generation of ligands that both are tailored to a given protein pocket
and exhibit a range of desired chemical properties is a major challenge in
structure-based drug design. Here, we propose an in-silico approach for the
$\textit{de novo}$ generation of 3D ligand structures using the equivariant
diffusion model PILOT, combining pocket conditioning with a large-scale
pre-training and property guidance. Its multi-objective trajectory-based
importance sampling strategy is designed to direct the model towards molecules
that not only exhibit desired characteristics such as increased binding
affinity for a given protein pocket but also maintains high synthetic
accessibility. This ensures the practicality of sampled molecules, thus
maximizing their potential for the drug discovery pipeline. PILOT significantly
outperforms existing methods across various metrics on the common benchmark
dataset CrossDocked2020. Moreover, we employ PILOT to generate novel ligands
for unseen protein pockets from the Kinodata-3D dataset, which encompasses a
substantial portion of the human kinome. The generated structures exhibit
predicted $IC_{50}$ values indicative of potent biological activity, which
highlights the potential of PILOT as a powerful tool for structure-based drug
design.
