Leveraging Side Information for Ligand Conformation Generation using
  Diffusion-Based Approaches
Jiamin Wu He Cao Yuan Yao
Ligand molecule conformation generation is a critical challenge in drug
discovery. Deep learning models have been developed to tackle this problem,
particularly through the use of generative models in recent years. However,
these models often generate conformations that lack meaningful structure and
randomness due to the absence of essential side information. Examples of such
side information include the chemical and geometric features of the target
protein, ligand-target compound interactions, and ligand chemical properties.
Without these constraints, the generated conformations may not be suitable for
further selection and design of new drugs. To address this limitation, we
propose a novel method for generating ligand conformations that leverage side
information and incorporate flexible constraints into standard diffusion
models. Drawing inspiration from the concept of message passing, we introduce
ligand-target massage passing block, a mechanism that facilitates the exchange
of information between target nodes and ligand nodes, thereby incorporating
target node features. To capture non-covalent interactions, we introduce
ligand-target compound inter and intra edges. To further improve the biological
relevance of the generated conformations, we train energy models using scalar
chemical features. These models guide the progress of the standard Denoising
Diffusion Probabilistic Models, resulting in more biologically meaningful
conformations. We evaluate the performance of SIDEGEN using the PDBBind-2020
dataset, comparing it against other methods. The results demonstrate
improvements in both Aligned RMSD and Ligand RMSD evaluations. Specifically,
our model outperforms GeoDiff (trained on PDBBind-2020) by 20% in terms of the
median aligned RMSD metric.