Leveraging Artificial Intelligence to Accelerate and Enable Antibody Discovery

Abstract

Monoclonal antibodies are a powerful, diverse class of therapeutics which can be developed to target theoretically any protein with exquisite specificity. Despite decades of effort toward experimental and computational methods for antibody discovery, existing approaches remain limited by low probabilities of success, insufficient datasets, and limited scope. In this dissertation I present three complementary methods which leverage artificial intelligence to overcome such limitations, providing a novel computational framework to augment the traditional antibody discovery process. First, I present a lightweight method for denoising antibody specificity predictions from single-cell sequencing data by clustering antigen counts into signal and noise components with a negative binomial mixture model. I show that this approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method, regardless of variance in data size and noise structure across samples. Next, I present MAGE (Monoclonal Antibody GEnerator), a sequence-based protein Large Language Model (LLM) fine-tuned for the task of generating paired variable heavy and light chain antibody sequences against targets of interest. I show that MAGE is capable of generating diverse antibody sequences with experimentally validated binding specificity and neutralization against three viral targets, without need for a starting antibody template. Lastly, I demonstrate that classification of antibody binding specificity is possible from sequence alone using LLM classifiers. I present two separate model architectures for this task, using the SARS-CoV-2 spike receptor binding domain (RBD) as a proof of concept, and explore the interpretability of these models. While each of these projects independently present novel methods for accelerating the discovery process, the synergistic integration of such methods presents a powerful framework for AI-augmented antibody discovery that enables iterative improvement in combination with experimental validation. This lab-in-the-loop environment will drastically decrease the resources necessary for antibody discovery, while also enabling the design of antibodies with novel properties, and against novel targets, expanding the capabilities of existing methods.