Bonobo: Efficient Library-Scale Generation for De Novo Antibody Design

Sebastian W. Ober, Nick Bhattacharya, Phillip Maffettone, Calvin McCarter, Hunter Elliott

Keywords: GFlowNets, RL, de novo design, antibodies, AlphaFold

TL;DR: We use an AlphaFold-based reward for designing antibody binders using GFlowNets

Abstract:

Recent developments in _de novo_ antibody design show promise for generating candidates that bind to drug targets.
Methods which have shown success in the lab propose designs with either a diffusion model or hallucination-based sequence optimization. They then filter these candidates with a structure prediction model.
Hallucination-based methods rely on expensive backpropagation through a loss function derived from the structure prediction model to perform per-generated-sequence optimization, limiting their ability to generate designs at the scale of the libraries, which may be needed for challenging targets.
We propose _Bonobo_, which instead trains a generative model on the structure prediction loss using a GFlowNet formulation in which we transform the loss to act as a reward function.
This does not require differentiation of the structure predictor, increasing our computational efficiency and unlocking a broader class of structure-based models for usage.
Crucially, our approach amortizes the cost of generation into training time, enabling Bonobo to generate library-scale sets of diverse sequences at a significantly lower cost.
We show empirically that our approach can effectively model complex loss functions and generate large numbers of high-performing novel antibody sequences for a range of target proteins.

Submission Number: 52