ToxiEval-ZKP: A Structure-Private Verification Framework for Molecular Toxicity Repair Tasks

TL;DR

ToxiEval-ZKP introduces a zero-knowledge proof framework for verifying molecular toxicity repair outputs, ensuring structural privacy while maintaining validation integrity in generative AI for molecular science.

Contribution

It is the first to incorporate ZKP mechanisms into molecular toxicity evaluation, enabling privacy-preserving verification of generated molecules.

Findings

Enables validation without revealing molecular structures

Offers efficient and secure ZKP-based verification system

Demonstrates effectiveness through experimental validation

Abstract

In recent years, generative artificial intelligence (GenAI) has demonstrated remarkable capabilities in high-stakes domains such as molecular science. However, challenges related to the verifiability and structural privacy of its outputs remain largely unresolved. This paper focuses on the task of molecular toxicity repair. It proposes a structure-private verification framework - ToxiEval-ZKP - which, for the first time, introduces zero-knowledge proof (ZKP) mechanisms into the evaluation process of this task. The system enables model developers to demonstrate to external verifiers that the generated molecules meet multidimensional toxicity repair criteria, without revealing the molecular structures themselves. To this end, we design a general-purpose circuit compatible with both classification and regression tasks, incorporating evaluation logic, Poseidon-based commitment hashing, and a nullifier-based replay prevention mechanism to build a complete end-to-end ZK verification system. Experimental results demonstrate that ToxiEval-ZKP facilitates adequate validation under complete structural invisibility, offering strong circuit efficiency, security, and adaptability, thereby opening up a novel paradigm for trustworthy evaluation in generative scientific tasks.