Generator Disagreement Acquisition for Sample-Efficient Molecular Discovery

Goal-directed molecular design often emphasizes the best molecule found, whereas practical discovery campaigns require multiple promising candidates under a limited budget of expensive oracle evaluations. In parent-conditioned search, however, a parent's observed reward need not reflect its ability to generate valuable offspring. This motivates selecting parents using predicted offspring reward distributions rather than their own scores alone. We introduce Generator Disagreement Acquisition (GDA), a closed-loop framework that allocates oracle queries at the parent level. GDA samples offspring from each candidate parent and combines the mean and dispersion of their surrogate-predicted rewards to select which parents supply candidates for oracle evaluation. This dispersion, termed generator disagreement, measures variation in predicted rewards across offspring rather than disagreement among predictors for a fixed molecule. Oracle feedback updates both the surrogate and the parent pool. We evaluate GDA on six tasks across DOCKSTRING and GEAM. All methods start from the same 512 labeled molecules and receive 800 additional oracle evaluations. With random-forest ensembles, GDA improves mean top-1% hit yield over a screened genetic algorithm using the same proposal operator and surrogate family by 38-61% on DOCKSTRING and 16-33% on GEAM. It also improves hit yield over Augmented Memory by 27-69% on DOCKSTRING, while Augmented Memory remains competitive on two GEAM tasks. These results support parent-level acquisition from predicted offspring distributions as a sample-efficient strategy for identifying multiple high-scoring candidates for downstream evaluation.