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Rui Han

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4 papers

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EAAI Journal 2026 Journal Article

Fusion-driven graph representation enhancement for predicting interactions of new drugs

Jiankang Liu
Yuhan Zhao
Rui Han
Yihan Fu
Lin Wang

Accurate prediction of drug–drug interactions (DDIs) for newly synthesized compounds enables early, in-silico safety screening in drug discovery and formulary review. We target the cold-start regime, where (i) new compounds are topologically isolated on external biomedical knowledge graphs (KGs) and on the DDI graph, and (ii) sparse supervision hampers the learning of discriminative representations. We propose an early-fusion method (LINCS-DDI) that inserts shared substructure nodes to connect a molecular-fingerprint knowledge graph with the DDI graph, turning structural similarity into topological links, and providing two-hop connectivity directly from the Simplified Molecular Input Line Entry System (SMILES) without prior inclusion in external KGs. Building on this substrate, we introduce Native Dual-View Contrastive Learning (NDV-CL): within a single pass of a flow-based graph neural network (GNN), forward and reverse message-passing representations of the same drug pair are treated as deterministic positives, while label-guided negatives (screened using only training-split interaction labels) are mined within the induced subgraph, improving representation quality without stochastic augmentations. Under strict cold-start settings on two open-source datasets, LINCS-DDI improves macro-F1 by up to 4. 1% over the best baseline and reduces contrastive overhead by up to 66%. These properties make the approach suitable for routine, large-scale preclinical DDI triage, prioritizing high-risk combinations for wet-lab validation, and informing pharmacovigilance pipelines.

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AAAI Conference 2026 Conference Paper

LLM-Guided Quantified SMT Solving over Uninterpreted Functions

Kunhang Lv
Yuhang Dong
Rui Han
Fuqi Jia
Feifei Ma
Jian Zhang

Quantified formulas with Uninterpreted Functions (UFs) over non-linear real arithmetic pose fundamental challenges for Satisfiability Modulo Theories (SMT) solving. Traditional quantifier instantiation methods struggle because they lack semantic understanding of UF constraints, forcing them to search through unbounded solution spaces with limited guidance. We present AquaForte, a framework that leverages Large Language Models to provide semantic guidance for UF instantiation by generating instantiated candidates for function definitions that satisfy the constraints, thereby significantly reducing the search space and complexity for solvers. Our approach preprocesses formulas through constraint separation, uses structured prompts to extract mathematical reasoning from LLMs, and integrates the results with traditional SMT algorithms through adaptive instantiation. AquaForte maintains soundness through systematic validation: LLM-guided instantiations yielding SAT solve the original problem, while UNSAT results generate exclusion clauses for iterative refinement. Completeness is preserved by fallback to traditional solvers augmented with learned constraints. Experimental evaluation on SMT-COMP benchmarks demonstrates that AquaForte solves numerous instances where state-of-the-art solvers like Z3 and CVC5 timeout, with particular effectiveness on satisfiable formulas. Our work shows that LLMs can provide valuable mathematical intuition for symbolic reasoning, establishing a new paradigm for SMT constraint solving.

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AAAI Conference 2025 Conference Paper

A Complete Algorithm for Optimization Modulo Nonlinear Real Arithmetic

Fuqi Jia
Yuhang Dong
Rui Han
Pei Huang
Minghao Liu
Feifei Ma
Jian Zhang

Optimization Modulo Nonlinear Real Arithmetic, abbreviated as OMT(NRA), generally focuses on optimizing a given objective subject to quantifier-free Boolean combinations of primitive constraints, including Boolean variables, polynomial equations, and inequalities. It is widely applicable in areas like program verification, analysis, planning, and so on. The existing solver, OptiMathSAT, officially supporting OMT(NRA), employs an incomplete algorithm. We present a sound and complete algorithm, Optimization Cylindrical Algebraic Covering (OCAC), integrated within the Conflict-Driven Clause Learning (CDCL) framework, specifically tailored for OMT(NRA) problems. We establish the correctness and termination of CDCL(OCAC) and explore alternative approaches using cylindrical algebraic decomposition (CAD) and first-order formulations. Our work includes the development of the first complete OMT solver for NRA, demonstrating significant performance improvements. In benchmarks generated from SMT-LIB instances, our algorithm finds the optimum value in about 150% more instances compared to the current leading solver, OptiMathSAT.

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YNIMG Journal 2025 Journal Article

The brain-gut microbiota network (BGMN) is correlated with symptom severity and neurocognition in patients with schizophrenia

Runlin Peng
Wei Wang
Liqin Liang
Rui Han
Yi Li
Haiyuan Wang
Yuran Wang
Wenhao Li

The association between the human brain and gut microbiota, known as the "brain-gut-microbiota axis", is involved in the neuropathological mechanisms of schizophrenia (SZ); however, its association patterns and correlations with symptom severity and neurocognition are still largely unknown. In this study, 43 SZ patients and 55 normal controls (NCs) were included, and resting-state functional magnetic resonance imaging (rs-fMRI) and gut microbiota data were acquired for each participant. First, the brain features of brain images and functional brain networks were computed from rs-fMRI data; the gut features of gut microbiota abundance and the gut microbiota network were computed from gut microbiota data. Second, we propose a novel methodology to construct an individual brain-gut microbiota network (BGMN) for each participant by combining the brain and gut features via multiple strategies. Third, discriminative models between SZ patients and NCs were built using the connectivity matrices of the BGMN as input features. Moreover, the correlations between the most discriminative features and the scores of symptom severity and neurocognition were analyzed in SZ patients. The results showed that the best discriminative model between SZ patients and NCs was achieved using the connectivity matrices of the BGMN when all the brain and gut features were integrated, with an accuracy of 0.90 and an area under the curve value of 0.97. The most discriminative features were related primarily to the genera Faecalibacterium and Collinsella, in which the genus Faecalibacterium was linked to the visual system and subcortical cortices and the genus Collinsella was linked to the default network and subcortical cortices. Furthermore, parts of the most discriminative features were significantly correlated with the scores of neurocognition in the SZ patients. The methodology for constructing individual BGMNs proposed in this study can help us reveal the associations between the brain and gut microbiota and understand the neuropathology of SZ.

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