Research map / Probabilistic and scientific modeling / Bayesian optimization
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use LaMBO instead of genetic optimizers | sequence design optimization · Bigrams, logP + QED, DRD3 docking + SA, Stability + SASA | relative hypervolume improvement | Helped |
| use DAE-generated proposals instead of uniform mutation proposals (ablation) | model-based GA + MTGP + NEHVI · Bigrams, logP + QED, DRD3 docking + SA, Stability + SASA | relative hypervolume improvement | Helped |
| optimize DAE proposals by gradient descent on the query loss (ablation) | LaMBO · Bigrams | relative hypervolume improvement | Helped |
| add proposal entropy penalty (ablation) | LaMBO · Bigrams | relative hypervolume improvement | Hurt |
| use NEHVI instead of averaged normalized objectives (ablation) | LaMBO · Stability + SASA | hypervolume: 1.51 -> 1.57 | Helped |
| use LaMBO rather than LSBO | single-objective sequence optimization · penalized logP | objective value: 27.84 -> 50 | Helped |
| optimize fluorescent protein stability and SASA with LaMBO | RFP variants · in vitro protein design | number of ancestor proteins with discovered non-dominated variants: three of the five | Helped |
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