Bayesian Optimization for General Reaction Conditions
Authors: Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alán Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, +1 more
Organizations: Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich CH-8093, Switzerland · NCCR Catalysis, Switzerland · Department of Chemistry, University of Toronto, Toronto, Canada · Vector Institute, Toronto, Canada · Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA, USA · Dana-Farber Cancer Institute, Boston, MA, USA · School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore · Department of Computer Science, University of Toronto, Toronto, Canada · Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada · Department of Materials Science and Engineering, University of Toronto, Toronto, Canada · Acceleration Consortium, University of Toronto, Toronto, Canada · Canadian Institute for Advanced Research (CIFAR) · Institute of Medical Science, Medical Sciences Building, Toronto, Canada · NVIDIA, Toronto, Canada · Department of Computer Science, Western University, London, Canada · School of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and high-throughput experimentation. However, identifying such conditions efficiently has been a longstanding challenge, as it requires decision making under uncertainty with respect to both conditions and substrates, while minimizing the number of required experiments. Here, we introduce CurryBO, a high-level framework for generality-oriented optimization. By formalizing the problem as Bayesian optimization over curried functions, CurryBO provides a unified framework that accommodates different generality definitions (e.g., mean yield across substrates), and supports a range of substrate and condition selection strategies. We evaluate this framework on four benchmark tasks in experimental reaction optimization, and systematically analyze key algorithmic components. Our results show that efficient experiment planning can be achieved by emphasizing exploration when selecting reaction conditions, followed by the uncertainty-guided prioritization of substrates in a sequential decison-making scheme. Based on these insights, we design and validate an optimization policy that substantially improves sample efficiency relative to previously reported approaches across all benchmarks. Overall, the flexibility and modularity of CurryBO facilitate the integration of generality-oriented optimization into experimental settings, enabling more efficient identification of solutions that perform robustly across diverse tasks.
When applying Bayesian optimization (BO) to scientific workflow, a major yet often overlooked source of uncertainty is the task itself -- namely, what to optimize and how to evaluate it -- which can evolve as evidence accumulates. We introduce Generate-Select-Refine (GSR), a open-ended BO framework that alternates between task generation and task optimization. Starting from a user-provided seed task, GSR generates new tasks in a coarse-to-fine manner while a task-acquisition function schedules optimization. Asymptotically, it concentrates evaluations on the best task, incurring only logarithmic regret overhead relative to single-task BO. We apply GSR to new product development, chemical synthesis scaling, algorithm analysis, and patent repurposing, where it outperforms existing LLM-based optimizers.
Bayesian optimization (BO) is widely used for expensive black-box problems, yet practical performance depends not only on high-level algorithmic choices but also on how surrogate model training, input and output warping transformations, acquisition functions, and candidate search are implemented. We present tidyHEBO, a BoTorch-native single-objective optimizer designed for robust general-purpose optimization. tidyHEBO jointly fits Yeo-Johnson output warping with the Gaussian-process surrogate, evaluates acquisition functions on the original objective scale using deterministic quadrature or MC-samples, and performs constrained cumulative Pareto search over multiple acquisition criteria. Without any Olympus-specific hyperparameter tuning - using only default optimizer configurations - tidyHEBO ranked first among the evaluated methods on the Olympus benchmark. It achieved the best average ranks for typical performance (average rank 1.53), worst-tail performance (1.21), and run-to-run variability (2.00), measured by median nAUC, CVaR_nAUC, and IQR_nAUC, respectively. Using the same default configuration, tidyHEBO also performed strongly on synthetic and Needle-in-a-Haystack problems and closely matched HEBO on Bayesmark (92.64 versus 93.34) while exceeding GP with logarithmic expected improvement and random search. Adaptive batching reduced feedback rounds while revealing a controllable trade-off between parallelization and optimization quality as the batch cap increased. These results characterize tidyHEBO as a robust, reproducible general-purpose optimizer for a broad range of practical optimization problems, including scientific applications and hyperparameter tuning.
Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly. The practical impact of an optimization method therefore hinges on its sample efficiency: how few evaluations it needs to find strong candidates. We introduce Sample Efficient Generative Optimization (SEGO), a framework for Bayesian optimization on adaptively generated molecules. In SEGO, a probabilistic surrogate model forms a hypothesis about where hits lie in chemical space, a generative model is steered to propose candidates in that region, the most promising candidate is selected via an acquisition function, and the resulting oracle call is used both to sharpen the surrogate and to anchor the generator in real reward. SEGO attains state-of-the-art performance on the practical molecular optimization (PMO) benchmark using only one tenth of the oracle calls consumed by other methods, and on a multiparameter docking task it reaches ten hits in roughly half the oracle calls of existing approaches. These gains move molecular optimization closer to campaigns driven by direct experimental feedback.