Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation

The convergence of biotechnology and artificial intelligence
has
the potential to transform drug development, especially in the field
of therapeutic peptide design. Peptides are short chains of amino
acids with diverse therapeutic applications that offer several advantages
over small molecular drugs, such as targeted therapy and minimal side
effects. However, limited oral bioavailability and enzymatic degradation
have limited their effectiveness. With advances in deep learning techniques,
innovative approaches to peptide design have become possible. In this
work, we demonstrate HYDRA, a hybrid deep learning approach that leverages
the distribution modeling capabilities of a diffusion model and combines
it with a binding affinity maximization algorithm that can be used
for de novo design of peptide binders for various target receptors.
As an application, we have used our approach to design therapeutic
peptides targeting proteins expressed by Plasmodium
falciparum erythrocyte membrane protein 1 (PfEMP1)
genes. The ability of HYDRA to generate peptides conditioned on the
target receptor’s binding sites makes it a promising approach
for developing effective therapies for malaria and other diseases.