google Smarter nucleic acid design with NucleoBench and AdaBeam (opens in new tab)
Google Research and Move37 Labs have introduced NucleoBench, a comprehensive open-source benchmark for nucleic acid design, alongside AdaBeam, a high-performing new optimization algorithm. While AI models have become highly proficient at predicting the biological properties of DNA and RNA, generating optimal sequences within massive search spaces—such as the $2 \times 10^{120}$ possible variations for a 5' UTR—remains a significant hurdle. By standardizing evaluation across 16 distinct biological tasks, this research identifies AdaBeam as a superior method that scales effectively to the large-scale models required for modern drug discovery. ## Standardizing the Optimization Pipeline The process of computational nucleic acid design typically follows a five-step workflow: data collection, training a predictive model, generating candidate sequences (the design step), wet-lab validation, and iterative retraining. NucleoBench focuses specifically on the design step, which has historically lacked standardized evaluation. * Most existing benchmarks rely on decades-old methods like simulated annealing or vanilla genetic algorithms. * Traditional algorithms often treat predictive models as "black boxes," failing to leverage internal model data to guide the search. * The vastness of genomic search spaces makes brute-force optimization impossible, necessitating more intelligent, model-aware generation strategies. ## The NucleoBench Framework NucleoBench is the first large-scale benchmark designed to compare gradient-free and gradient-based design algorithms under identical conditions. The framework encompasses over 400,000 experiments to ensure statistical rigor across diverse biological challenges. * **Algorithm Categories**: It compares gradient-free methods (like directed evolution), which are simple but ignore model internals, against gradient-based methods (like FastSeqProp), which use the model’s internal "direction of steepest improvement" to find better sequences. * **Task Diversity**: The 16 tasks include controlling gene expression in specific cell types (liver or neuronal), maximizing transcription factor binding, and improving chromatin accessibility. * **Scale**: The benchmark includes long-range DNA sequence challenges using large-scale models like Enformer, which are computationally demanding but critical for understanding complex genomic interactions. ## AdaBeam’s Hybrid Optimization Performance Drawing on insights from the NucleoBench evaluation, the researchers developed AdaBeam, a hybrid algorithm that combines the strengths of various optimization strategies. * **Success Rate**: AdaBeam outperformed existing algorithms on 11 of the 16 tasks in the benchmark. * **Efficiency and Scaling**: Unlike many gradient-based methods that struggle with computational overhead, AdaBeam demonstrates superior scaling properties as sequences become longer and predictive models grow in complexity. * **Methodology**: It functions as a hybrid approach, using sophisticated search techniques to navigate the sequence space more effectively than "vanilla" algorithms developed before the era of deep learning. The researchers have made AdaBeam and the NucleoBench repository freely available to the scientific community. By providing a standardized environment for testing, they aim to accelerate the development of next-generation treatments, including more stable mRNA vaccines and precise CRISPR gene therapies.