ConfHit: Conformal Generative Design with Oracle Free Guarantees

TL;DR

ConfHit is a novel, distribution-free framework that provides reliable statistical guarantees for generative models in drug discovery, ensuring generated candidates meet desired properties without requiring oracle access.

Contribution

It introduces a conformal prediction-based method that guarantees the presence of at least one valid candidate and refines generation sets without oracle access, under distribution shift.

Findings

ConfHit guarantees at least one hit with high confidence.

It maintains compact, valid candidate sets across various tasks.

The framework is broadly applicable to generative molecule design.

Abstract

The success of deep generative models in scientific discovery requires not only the ability to generate novel candidates but also reliable guarantees that these candidates indeed satisfy desired properties. Recent conformal-prediction methods offer a path to such guarantees, but its application to generative modeling in drug discovery is limited by budget constraints, lack of oracle access, and distribution shift. To this end, we introduce ConfHit, a distribution-free framework that provides validity guarantees under these conditions. ConfHit formalizes two central questions: (i) Certification: whether a generated batch can be guaranteed to contain at least one hit with a user-specified confidence level, and (ii) Design: whether the generation can be refined to a compact set without weakening this guarantee. ConfHit leverages weighted exchangeability between historical and generated samples to eliminate the need for an experimental oracle, constructs multiple-sample density-ratio weighted conformal p-value to quantify statistical confidence in hits, and proposes a nested testing procedure to certify and refine candidate sets of multiple generated samples while maintaining statistical guarantees. Across representative generative molecule design tasks and a broad range of methods, ConfHit consistently delivers valid coverage guarantees at multiple confidence levels while maintaining compact certified sets, establishing a principled and reliable framework for generative modeling.