A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development
arXiv:2606.19230v1 Announce Type: new Abstract: This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-derived quantities are reformulated as objectives of a multi-objective optimization problem, and the resulting Pareto front is exposed to a domain expert for interactive candidate selection rather than returning a single automated recommendation. The framework is extended in two directions: constrained optimization is addressed by incorporating the posterior probability of
The increasing complexity and data intensiveness of bioprocess development, coupled with advances in AI like Bayesian optimization, necessitate more efficient and human-guided approaches.
This development allows for faster, more reliable, and constrained-aware optimization of bioprocesses, critical for drug discovery, advanced materials, and sustainable manufacturing.
Bioprocess development transitions from purely automated or trial-and-error methods to intelligent, human-in-the-loop systems that can incorporate expert knowledge and real-world constraints.
- · Biopharmaceutical companies
- · Biotech startups
- · Synthetic biology researchers
- · AI/ML platform providers
- · Traditional bioprocess development consultancies
- · Companies reliant on slow, manual optimization
- · Inefficient R&D labs
Bioprocess development cycles will accelerate, reducing time and cost for new biological products.
This acceleration will lead to a faster commercialization of therapeutics, sustainable chemicals, and advanced biomaterials.
The enhanced efficiency in synthetic biology could enable novel industrial applications and potentially address global challenges in health, energy, and resources.
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Read at arXiv cs.LG