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Yanchao Tan

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Aug 2026

HyperMolFusion: A Hypergraph-Enhanced Multi-Modal Fusion Framework for Accurate Molecular Property Prediction.

Molecular property prediction is a critical task in accelerating drug discovery. While deep learning has shown promise, prevailing single-modal methods struggle to integrate multi-source (e.g., atomic graph and molecular fingerprints), heterogeneous chemical knowledge, thereby failing to holistically represent molecular structures and capture the high-order synergistic interactions governing their functions. To address these challenges, we present HyperMolFusion, a hypergraph-enhanced multi-modal fusion model for molecular property prediction. Compared with traditional graphs limited to pairwise atomic bonds, HyperMolFusion models chemical motifs as hyperedges to explicitly capture high-order structural correlations and encode complex molecular interactions. The framework comprises three core representation learning modules: AtomConv for local atomic interaction learning via attention-enhanced message passing, HyperConv for motif-level high-order correlation extraction via hypergraph convolution with GRU gating, and a mixed molecular fingerprint module that adaptively integrates MACCS, PubChem, and Pharmacophore fingerprints. A chemically guided attention (CGA) mechanism then dynamically fuses these multi-level features into hierarchical molecular representations, alleviating over-smoothing and preserving structural information effectively. Evaluated on eight MoleculeNet benchmarks (covering regression and classification tasks), HyperMolFusion achieves promising performance. For regression, it achieves an RMSE of 0.611 in lipophilicity, 0.653 in ESOL, and 0.951 in FreeSolv. For classification, it achieves a ROC-AUC of 0.935 in ClinTox, 0.907 in BBBP, and 0.689 in SIDER. This work provides a systematic and effective solution for molecular property prediction: by holistically integrating atomic, motif, and global fingerprint information via hypergraph modeling, HyperMolFusion offers a more reliable computational tool to enhance the efficiency and accuracy of drug development pipelines.

Yawen Lin, Sheng Lian, Shaoxin Bian et al. · 0 citations
Book Open access Jul 2026

PurifAI: Detecting and Fixing Search-Induced Distortions in Web-Augmented LLMs

As Large Language Models (LLMs) increasingly serve as interfaces for proprietary data (e.g., enterprise knowledge bases, legal statutes), ensuring their fidelity to trusted internal information is paramount. While integrating real-time web search can enhance model utility, it introduces a critical vulnerability: the ingestion of conflicting, misleading, or hallucinated content from the open web can override the model's adherence to its verified internal knowledge. We define this failure mode as search-induced distortion, a significant risk in high-stakes domains where the internal knowledge base serves as the absolute ground truth. To address this challenge, we present PurifAI, a proactive, model-agnostic, cache-level purification system designed for safety- and compliance-sensitive deployments. Rather than serving as a general fact-checking engine, PurifAI is explicitly designed to preserve knowledge alignment with a pre-defined trusted knowledge core. It automatically generates diagnostic probes from trusted documents to identify and neutralize searched web content that conflicts with the canonical internal source before such content distorts the LLM's responses. Extensive evaluations on news, encyclopedic, and legal domains show that PurifAI effectively improves alignment with the trusted core, identifying and blocking distortive content at the source and achieving repair success rates above 70% across mainstream LLMs. Our work offers a practical safeguard for enterprises and other high-stakes applications seeking to integrate web-augmented LLMs without compromising policy consistency and trusted knowledge integrity.

Guoqing Wang, Zhao Zhang, Zeyu Sun et al. · 0 citations