BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
Jackie Rao, Ferran Gonzalez Hernandez, Leon Gerard, Alexandra Gessner
TLDR
BOAT is a Bayesian optimization framework that efficiently optimizes multiple antibody properties for drug design, outperforming other methods.
Key contributions
- Introduces BOAT, a versatile Bayesian optimization framework for multi-property antibody engineering.
- Couples uncertainty-aware surrogate modeling with a genetic algorithm for joint optimization.
- Efficiently explores antibody sequence space to balance multiple drug-like properties.
- Outperforms state-of-the-art generative methods in specific optimization regimes.
Why it matters
Antibody design is a complex multi-objective problem. BOAT offers an efficient, 'plug-and-play' solution to jointly optimize multiple properties, accelerating the discovery of viable drug candidates. This framework helps overcome resource-intensive sequential filtering, making drug development faster and more effective.
Original Abstract
Antibody lead optimization is inherently a multi-objective challenge in drug discovery. Achieving a balance between different drug-like properties is crucial for the development of viable candidates, and this search becomes exponentially challenging as desired properties grow. The ever-growing zoo of sophisticated in silico tools for predicting antibody properties calls for an efficient joint optimization procedure to overcome resource-intensive sequential filtering pipelines. We present BOAT, a versatile Bayesian optimization framework for multi-property antibody engineering. Our `plug-and-play' framework couples uncertainty-aware surrogate modeling with a genetic algorithm to jointly optimize various predicted antibody traits while enabling efficient exploration of sequence space. Through systematic benchmarking against genetic algorithms and newer generative learning approaches, we demonstrate competitive performance with state-of-the-art methods for multi-objective protein optimization. We identify clear regimes where surrogate-driven optimization outperforms expensive generative approaches and establish practical limits imposed by sequence dimensionality and oracle costs.
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