ToxiEval-ZKP: Molecular Toxicity Verification

• The paper demonstrates the application of zero-knowledge proofs to verify toxicity repair thresholds, ensuring molecular structural privacy.
• It employs Poseidon-based commitment hashing and a nullifier mechanism to secure data integrity and prevent replay attacks.
• The framework offers an end-to-end solution for both classification and regression tasks in molecular toxicity repair.

ToxiEval-ZKP is a structure-private verification framework for molecular toxicity repair tasks that applies zero-knowledge proofs to the evaluation stage of generative molecular design. Its stated purpose is to allow model developers to convince external verifiers that generated molecules satisfy multidimensional toxicity repair criteria without revealing the molecular structures themselves. The framework is presented as a full end-to-end ZK verification system that combines evaluation logic, Poseidon-based commitment hashing, and a nullifier-based replay prevention mechanism, and is described as the first introduction of ZKP mechanisms into the evaluation process of molecular toxicity repair (Lin et al., 16 Aug 2025).

1. Concept and problem setting

The framework is motivated by two constraints in generative molecular science. The first is verifiability: in high-stakes domains such as molecular design and drug discovery, third parties may need assurance that model-generated molecules satisfy scientific criteria. The second is structural privacy: the molecular structure is treated as sensitive intellectual property, so disclosure can create patent and trade secret risks. ToxiEval-ZKP addresses these constraints in the specific setting of molecular toxicity repair, where toxic molecules are transformed into non-toxic ones and then evaluated against multiple criteria, including toxicity-related and drug-likeness-related properties (Lin et al., 16 Aug 2025).

Within that setting, the framework supports provable claims of compliance with threshold-based criteria such as “LD50 > 800”, “QED ≥ 0.5”, and “SAS ≤ 6” without exposing SMILES strings or metric values. The paper characterizes this as “complete structural invisibility”: the verifier checks a proof of threshold satisfaction, rather than inspecting either the molecule or the underlying evaluation outputs. In practical terms, the system shifts trust from direct disclosure to cryptographic attestations over normalized evaluation data.

2. System architecture and end-to-end workflow

ToxiEval-ZKP is organized around four core components: a Data Processor, a Proof Generator, a Proof Verifier, and a general-purpose verification circuit implemented in Circom. The architecture is explicitly end-to-end: it begins with extraction and normalization of molecular evaluation metrics and ends with verifier-side checking of a zk-SNARK proof, a commitment, and a nullifier (Lin et al., 16 Aug 2025).

The workflow is specified in seven stages. First, a metric vector $\mathbf{v}$ containing structural and toxicity metrics is extracted and scaled to integers. Second, a random salt $s$ is sampled for privacy amplification in commitments. Third, the circuit checks each metric against task-specific thresholds. Fourth, the system computes a Poseidon hash over $\mathbf{v}$ and $s$ to produce a structure-committed value. Fifth, it computes a second Poseidon hash over the commitment and the task type $t$ to obtain a replay-prevention nullifier. Sixth, a zk-SNARK proof $\pi$ is generated from the circuit logic and inputs. Seventh, the verifier receives $\pi$, the commitment, and the nullifier, checks the proof, and rejects it if the nullifier has already been used.

This organization makes the verification interface narrow and cryptographic. The verifier does not re-execute the scientific evaluation externally; instead, it checks that the prover executed the evaluation logic inside the proof system and bound the hidden inputs to a commitment and task-specific nullifier.

3. Circuit semantics and supported task types

A central design objective of ToxiEval-ZKP is generality across both classification and regression settings. The paper states that the circuit is compatible with both task families and achieves this by normalizing evaluation metrics to integer inputs suitable for zk-SNARK processing. The private inputs are the metric vector and salt, the public inputs are the task type and threshold vector, and the public outputs are a verification result flag, a commitment, and a nullifier (Lin et al., 16 Aug 2025).

The private input vector is given as

$$\mathbf{v} = \{v_{\text{valid}}, v_{\text{safe}}, v_{\text{QED}}, v_{\text{SAS}}, v_{\text{Lip}}, v_{\text{Sim}}\},$$

together with the salt $s$. The public input includes the task type

$t \in \{0,1\},$

where $s$0 denotes classification and $s$1 denotes regression, and the threshold vector

$s$2

The paper expresses the core decision rule as

$s$3

It then specifies task-dependent behavior. For classification, with $s$4, $s$5 must exactly equal $s$6, which maps to the “non-toxic” class. For regression, with $s$7, the circuit enforces

$s$8

All checks are implemented through integer fixed-point comparators, and Boolean AND gates combine the per-metric predicates so that all thresholds must be satisfied simultaneously.

The use of a single circuit family across both binary toxicity repair and continuous toxicity endpoints is a substantive design choice. It indicates that ToxiEval-ZKP is not limited to one benchmark formulation; rather, it is intended as a reusable verification layer for multiple toxicity repair regimes.

4. Commitment construction, nullifiers, and structure privacy

The framework’s privacy model is built around two Poseidon-based objects: a commitment and a nullifier. The commitment binds the hidden metric vector and salt, while the nullifier binds the commitment to the task type for replay prevention. Both are designed for efficient use inside arithmetic circuits, which is why the system adopts Poseidon rather than a conventional hash optimized for non-ZK environments (Lin et al., 16 Aug 2025).

The commitment is defined as

$s$9

and the nullifier as

$\mathbf{v}$0

The paper describes Poseidon as zk-SNARK friendly and states that the commitment is a collision-resistant, privacy-preserving summary of the private evaluation metrics and random salt. By committing to all metrics and the salt, the system preserves auditability and future consistency checks without disclosing the underlying values. The salt serves as privacy amplification: identical molecules hashed with different salts yield different commitments, which mitigates brute-force attacks.

The nullifier mechanism addresses a distinct threat model. Because the verifier receives a commitment and proof rather than the underlying molecular structure, a replay-prevention primitive is needed to stop re-submission of the same proof artifact under the same task type. ToxiEval-ZKP therefore treats the nullifier as a one-use marker: if the nullifier is reused, the proof is rejected. A common misunderstanding is that structure-private verification eliminates all public outputs; in this framework, it does not. The task type, threshold vector, verification result flag, commitment, and nullifier remain public, while the metric vector and molecular structure remain hidden.

5. Proof system, security properties, and efficiency considerations

ToxiEval-ZKP uses zk-SNARKs via the Groth16 protocol over the BN128 curve. In the framework’s description, zero-knowledge is used to demonstrate compliance with all evaluation thresholds without leaking the metric vector $\mathbf{v}$1 or the underlying molecular structure. The paper further reports that the system facilitates adequate validation under complete structural invisibility and exhibits strong circuit efficiency, security, and adaptability (Lin et al., 16 Aug 2025).

The security claims in the description are tied to three mechanisms. First, threshold evaluation occurs inside the circuit, so success of verification attests to criterion satisfaction rather than to externally asserted scores. Second, the commitment binds the hidden inputs in a privacy-preserving way. Third, the nullifier prevents replay of previously accepted submissions. Together, these components define what the paper presents as a complete end-to-end ZK verification system for toxicity repair evaluation.

The paper does not, in the provided summary, enumerate detailed proving and verification latencies. A plausible implication, however, follows from broader measurements of Groth16-based systems: proof generation is often the dominant scalability bottleneck, while proof verification remains nearly constant because of succinctness. That asymmetry is documented for Groth16 rollup systems on Ethereum, where proof generation time increases with batch size while verification time remains almost constant (Habib, 21 Mar 2025). This suggests that ToxiEval-ZKP’s “strong circuit efficiency” should be interpreted in the usual SNARK engineering sense: verifier-side checking can be compact and efficient, but prover-side cost remains sensitive to circuit complexity.

6. Position within the broader zero-knowledge verification landscape

ToxiEval-ZKP belongs to a broader class of domain-specific ZK verification systems that prove compliance properties over hidden data rather than disclosing the data itself. Related work includes a scholarship evaluation system in which credential authorities generate dimension-specific ZK proofs, students aggregate them off-chain, and smart contracts verify compliance with evaluation criteria without revealing raw scores or computational details (Chen et al., 29 Oct 2025). In another direction, PAC-private outsourced computation uses non-interactive ZKPs to prove both correctness of computation and proper application of privacy-preserving noise while keeping proprietary mechanisms confidential (Repetto et al., 12 Feb 2026). For graph databases, ZKGraph shows that complex query verification can be made practical by decomposing evaluation into fine-grained primitives optimized for PLONKish arithmetization (Wu et al., 1 Jul 2025).

Against that background, ToxiEval-ZKP is distinguished by its target domain and by the specific privacy object it protects: molecular structure. Its circuit is not framed around credential aggregation, PAC-noise attestation, or query-operator decomposition, but around thresholded molecular evaluation with structure commitments and task-bound nullifiers. Even so, a shared architectural pattern is visible across these systems: private data remain off-chain or otherwise hidden; a structured proof system attests to correct evaluation; and compact public artifacts support verification, audit, or replay resistance. This suggests that ToxiEval-ZKP is part of an emerging methodological family in which scientific or institutional evaluation procedures are recast as zero-knowledge compliance proofs.

Within generative scientific workflows, the framework’s significance lies in relocating trust from disclosure to cryptographic verification. Rather than asking a reviewer, collaborator, or regulator to inspect molecules directly, it enables acceptance decisions to be based on proofs that a hidden candidate satisfies the prescribed repair criteria. The paper characterizes this as opening a novel paradigm for trustworthy evaluation in generative scientific tasks (Lin et al., 16 Aug 2025).