2026-06-12原文2 min readUpdated: 2026-06-12

MARD: Mirror-Augmented Reasoning Distillation for Mechanism-Level Drug-Drug Interaction Prediction

This paper introduces a reproducible labeling and evaluation protocol for mechanism-level drug-drug interaction (DDI) prediction, featuring a 7-family/147-subtype taxonomy and leakage-safe cold-split strategies. It also presents MARD-7B, a model trained with three innovations: single-token KL divergence, PRM-weighted DPO, and a mechanism-aware retrieval channel. On the April 2026 DrugBank release, MARD-7B is the only system among 32 that maintains accuracy under drug-pair novelty, outperforming the best baseline by +13.9 pp and GPT-4o by +6.7 pp at ~1% of frontier API cost. Anti-memorization analysis suggests gains stem from structured pharmacological reasoning.

SourcearXiv Computational LinguisticsAuthor: Mohammadreza Riyazat, Vian Lelo, Rameen Jafri, Yumna Khan, Abeer Badawi

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Key points

Proposes a 7-family/147-subtype taxonomy and leakage-safe cold-split evaluation protocol for mechanism-level DDI prediction.
MARD-7B integrates single-token KL divergence, PRM-weighted DPO, and mechanism-aware retrieval for reasoning distillation.
In a 32-system comparison, MARD-7B uniquely maintains accuracy under drug-pair novelty, beating best baseline by 13.9% and GPT-4o by 6.7% at 1% cost.
Anti-memorization signature shows improved accuracy on rare drugs, indicating gains from structured reasoning rather than memorization.

Why it matters

This matters because proposes a 7-family/147-subtype taxonomy and leakage-safe cold-split evaluation protocol for mechanism-level DDI prediction.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

[2606.12578] MARD: Mirror-Augmented Reasoning Distillation for Mechanism-Level Drug-Drug Interaction Prediction

[Submitted on 10 Jun 2026]

Title:MARD: Mirror-Augmented Reasoning Distillation for Mechanism-Level Drug-Drug Interaction Prediction

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Abstract:Mechanism-level drug-drug interaction (DDI) prediction requires identifying which enzyme or pharmacodynamic axis is implicated, in which direction, and with which evidence -- not merely whether two drugs interact. We introduce a reproducible mechanism-level DDI labelling and evaluation protocol with a structured 7-family/147-subtype taxonomy, leakage-safe cold-split protocols, and auditable reasoning metrics for evaluating pharmacological prediction beyond flat interaction classification. We propose a pipeline that produces a 7B reasoning MARD (Mirror-Augmented Reasoning Distillation), combining three training innovations: a single-token KL divergence on direction tag that ties the model's prediction, per-loss PRM-weighted DPO with programmatic hard negatives, and a leakage-safe mechanism-aware retrieval channel. Process-reward step labels are automatically verifiable against DrugBank-structured fields, requiring no human or LLM judges. On the April-2026 DrugBank release, our MARD-7B is the only system in a 32-system comparison whose accuracy survives drug-pair novelty, beating the best baseline by +13.9 pp and GPT-4o by +6.7 pp at ~1% of frontier API cost. Further analysis reveals an anti-memorisation signature where accuracy improves on rarely seen drugs, suggesting that gain comes from structured pharmacological reasoning rather than drug-frequency memorisation. We release corpus, DDI-PRM, retrieval index, and training code.

Comments: 29 pages, 9 figures. Preprint

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Computation and Language (cs.CL)

Cite as: arXiv:2606.12578 [cs.CL]

(or arXiv:2606.12578v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.12578

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Mohammadreza Riyazat [view email] [v1] Wed, 10 Jun 2026 18:26:11 UTC (3,436 KB)

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