  Antibody therapeutics has been extensively studied in drug discovery and
development within the past decades. One increasingly popular focus in the
antibody discovery pipeline is the optimization step for therapeutic leads.
Both traditional methods and in silico approaches aim to generate candidates
with high binding affinity against specific target antigens. Traditional in
vitro approaches use hybridoma or phage display for candidate selection, and
surface plasmon resonance (SPR) for evaluation, while in silico computational
approaches aim to reduce the high cost and improve efficiency by incorporating
mathematical algorithms and computational processing power in the design
process. In the present study, we investigated different graph-based designs
for depicting antibody-antigen interactions in terms of antibody affinity
prediction using deep learning techniques. While other in silico computations
require experimentally determined crystal structures, our study took interest
in the capability of sequence-based models for in silico antibody maturation.
Our preliminary studies achieved satisfying prediction accuracy on binding
affinities comparing to conventional approaches and other deep learning
approaches. To further study the antibody-antigen binding specificity, and to
simulate the optimization process in real-world scenario, we introduced
pairwise prediction strategy. We performed analysis based on both baseline and
pairwise prediction results. The resulting prediction and efficiency prove the
feasibility and computational efficiency of sequence-based method to be adapted
as a scalable industry practice.
