# Nanodesigner: resolving the complex-CDR interdependency with iterative refinement

## TL;DR

NanoDesigner is a new AI-based tool that improves the design of nanobodies by iteratively refining the interaction between the antibody and its target protein.

## Contribution

NanoDesigner introduces an iterative framework using generative AI to resolve the interdependency between CDR design and complex prediction in nanobody development.

## Key findings

- NanoDesigner approximately doubles the success rate of de novo nanobody designs.
- The method uses an expectation maximization algorithm to iteratively refine docking and CDR generation.
- The tool outperforms existing methods in nanobody design through direct comparison.

## Abstract

Camelid heavy-chain only antibodies consist of two heavy chains and single variable domains (VHHs), which retain antigen-binding functionality even when isolated. The term “nanobody” is now more generally used for describing small, single-domain antibodies. Several antibody generative models have been developed for the sequence and structure co-design of the complementarity-determining regions (CDRs) based on the binding interface with a target antigen. However, these models are not tailored for nanobodies and are often constrained by their reliance on experimentally determined antigen–antibody structures, which are labor-intensive to obtain. Here, we introduce NanoDesigner, a tool for nanobody design and optimization based on generative AI methods. NanoDesigner integrates key stages—structure prediction, docking, CDR generation, and side-chain packing—into an iterative framework based on an expectation maximization (EM) algorithm. The algorithm effectively tackles an interdependency challenge where accurate docking presupposes a priori knowledge of the CDR conformation, while effective CDR generation relies on accurate docking outputs to guide its design. NanoDesigner approximately doubles the success rate of de novo nanobody designs through continuous refinement of docking and CDR generation.

We developed a novel method for the design and optimization of nanobodies using generative AI. We use an iterative approach to address the problem that design of CDRs relies on knowledge of a complex consisting of nanobody and protein target, and accurate prediction of the complex relies on knowledge of the CDRs. We demonstrate that our method improves over the state of the art by direct comparison.