  This work introduces GeoDirDock (GDD), a novel approach to molecular docking
that enhances the accuracy and physical plausibility of ligand docking
predictions. GDD guides the denoising process of a diffusion model along
geodesic paths within multiple spaces representing translational, rotational,
and torsional degrees of freedom. Our method leverages expert knowledge to
direct the generative modeling process, specifically targeting desired
protein-ligand interaction regions. We demonstrate that GDD significantly
outperforms existing blind docking methods in terms of RMSD accuracy and
physicochemical pose realism. Our results indicate that incorporating domain
expertise into the diffusion process leads to more biologically relevant
docking predictions. Additionally, we explore the potential of GDD for lead
optimization in drug discovery through angle transfer in maximal common
substructure (MCS) docking, showcasing its capability to predict ligand
orientations for chemically similar compounds accurately.
