  The rise of cost involved with drug discovery and current speed of which they
are discover, underscore the need for more efficient structure-based drug
design (SBDD) methods. We employ Generative Flow Networks (GFlowNets), to
effectively explore the vast combinatorial space of drug-like molecules, which
traditional virtual screening methods fail to cover. We introduce a novel
modification to the GFlowNet framework by incorporating trigonometrically
consistent embeddings, previously utilized in tasks involving protein
conformation and protein-ligand interactions, to enhance the model's ability to
generate molecules tailored to specific protein pockets. We have modified the
existing protein conditioning used by GFlowNets, blending geometric information
from both protein and ligand embeddings to achieve more geometrically
consistent embeddings. Experiments conducted using CrossDocked2020 demonstrated
an improvement in the binding affinity between generated molecules and protein
pockets for both single and multi-objective tasks, compared to previous work.
Additionally, we propose future work aimed at further increasing the geometric
information captured in protein-ligand interactions.
