DeepAI AI Chat
Log In Sign Up

PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design

by   Ji-won Park, et al.

Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences. Whereas it is possible to optimize the various properties of interest jointly using a multi-objective acquisition function, such as the expected hypervolume improvement (EHVI), this approach does not account for objectives with a hierarchical dependency structure. We consider a common use case where some regions of the Pareto frontier are prioritized over others according to a specified partial ordering in the objectives. For instance, when designing antibodies, we would like to maximize the binding affinity to a target antigen only if it can be expressed in live cell culture – modeling the experimental dependency in which affinity can only be measured for antibodies that can be expressed and thus produced in viable quantities. In general, we may want to confer a partial ordering to the properties such that each property is optimized conditioned on its parent properties satisfying some feasibility condition. To this end, we present PropertyDAG, a framework that operates on top of the traditional multi-objective BO to impose this desired ordering on the objectives, e.g. expression → affinity. We demonstrate its performance over multiple simulated active learning iterations on a penicillin production task, toy numerical problem, and a real-world antibody design task.


A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertainty

Bayesian optimization is a popular tool for data-efficient optimization ...

Multi-objective Bayesian Optimization using Pareto-frontier Entropy

We propose Pareto-frontier entropy search (PFES) for multi-objective Bay...

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

Bayesian optimization is a gold standard for query-efficient continuous ...

Sample-efficient Multi-objective Molecular Optimization with GFlowNets

Many crucial scientific problems involve designing novel molecules with ...

Multi-objective Influence Diagrams

We describe multi-objective influence diagrams, based on a set of p obje...

Multi-objective optimization of actuation waveform for high-precision drop-on-demand inkjet printing

Drop-on-demand (DOD) inkjet printing has been considered as one of promi...