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Yuansheng Liu

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9 papers

1 author row

JBHI Journal 2026 Journal Article

Graph-Informed and FiLM-Enhanced Multimodal Fusion for Myocardial Infarction Prediction

Xiantong Xiang
Longxiao Gao
Yuansheng Liu
Ningji Gong
Yongshun Gong

Accurate and timely diagnosis of cardiovascular diseases, particularly myocardial infarction (MI), remains a critical clinical challenge. Existing electrocardiogram (ECG) analysis methods often rely solely on a single data modality, such as raw signals or waveform images, which limits their ability to capture the broader physiological context. To address this limitation, we propose GFM-MIP, a Graph-informed and FiLM-enhanced Multimodal Fusion framework for myocardial infarction prediction. GFM-MIP integrates 12-lead ECG time-series signals, ECG images, and laboratory test results through a unified architecture. Specifically, it employs a Graphormer encoder to model inter-lead dependencies in ECG signals and a Vision Transformer to extract morphological patterns from ECG images, both modulated by patient-specific laboratory features using Feature-wise Linear Modulation (FiLM). A Transformer-based fusion module captures cross-modal interactions, while a contrastive learning objective encourages alignment between signal and image modalities. Experimental results on a real-world clinical dataset and three public benchmarks demonstrate that GFM-MIP consistently outperforms state-of-the-art baselines across multiple evaluation metrics. Ablation studies further validate the contribution of each modality and architectural component. The proposed framework offers a clinically meaningful and scalable solution for robust, multimodal cardiovascular diagnosis.

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AAAI Conference 2026 Conference Paper

TRACE: Transformation-Aware Graph Refinement for Reaction Condition Prediction

Yujie Chen
Tengfei Ma
Yuansheng Liu
Leyi Wei
Shu Wu
Dongsheng Cao
Yiping Liu
Xiangxiang Zeng

Identifying suitable reaction conditions is critical for chemical synthesis, as they directly affect yield, selectivity, and transformation feasibility. While recent methods have shown promising results, most approaches either encode reactants and products independently or rely on rule-based reaction graphs, both of which constrain the ability of the model to capture condition-relevant structural transformations. In this work, we propose TRACE, a transformation-aware graph refinement framework for reaction condition prediction. TRACE constructs atom-level joint graphs that integrate both reactant and product structures to represent condition-relevant transformations. A structure-aware encoder enriches atom features with local chemical context, followed by a dynamic interaction refinement module that adaptively infers task-specific edges. To further guide the model toward condition-relevant patterns, a mechanism regularized graph encoder incorporates reaction center information, enabling more accurate modeling of transformation mechanisms. Experiments on benchmark datasets show that TRACE achieves state-of-the-art performance across multiple condition types. The integration of transformation-aware refinement leads to improvements in prediction accuracy and generalization, while maintaining robust performance in challenging and realistic synthesis planning scenarios.

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JBHI Journal 2025 Journal Article

Collaborative Learning Macroscopic Binding Trends and Microscopic Residue Interactions to Predict Peptide-Protein Interactions

Li Zeng
Yang Liu
Zu-guo Yu
Guosheng Han
Yuansheng Liu

Short peptides and their structural modifications have demonstrated significant potential in the field of therapeutic drug development. During the research and development process, peptide-protein interaction plays a crucial role for screening highly effective peptides. Although traditional experimental methods can identity peptide-protein interactions, their time-consuming and resource-intensive nature make researchers develop various of computational alternatives. In addition, accurately predicting these interactions necessitates both the macroscopic molecular binding affinity and the precise interaction patterns at the microscopic residue level. Existing computational methods face limitations, as they are typically confined to modeling at a single level, resulting in restricted prediction accuracy. To address this gap, we propose MMPepPro, a dual-level biofeature collaborative interaction learning framework that integrates macro-level binding trends with micro-level residue interaction features. Trained on 19, 187 peptide-protein complexes, MMPepPro combines molecular-level and amino acid-level features to achieve comprehensive modeling. Experimental validation demonstrates the model's superior performance across all evaluation metrics compared to other state-of-the-art methods in peptide-protein interaction prediction. More notably, its generalization performance across other four datasets validates the universality of this method, which will aid in the development of peptide-protein drugs.

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AAAI Conference 2025 Conference Paper

Multi-Objective Molecular Design Through Learning Latent Pareto Set

Yiping Liu
Jiahao Yang
Xuanbai Ren
Zhang Xinyi
Yuansheng Liu
Bosheng Song
Xiangxiang Zeng
Hisao Ishibuchi

Molecular design inherently involves the optimization of multiple conflicting objectives, such as enhancing bio-activity and ensuring synthesizability. Evaluating these objectives often requires resource-intensive computations or physical experiments. Current molecular design methodologies typically approximate the Pareto set using a limited number of molecules. In this paper, we present an innovative approach, called Multi-Objective Molecular Design through Learning Latent Pareto Set (MLPS). MLPS initially utilizes an encoder-decoder model to seamlessly transform the discrete chemical space into a continuous latent space. We then employ local Bayesian optimization models to efficiently search for local optimal solutions (i.e., molecules) within predefined trust regions. Using surrogate objective values derived from these local models, we train a global Pareto set learning model to understand the mapping between direction vectors (called “preferences”) in the objective space and the entire Pareto set in the continuous latent space. Both the global Pareto set learning model and local Bayesian optimization models collaborate to discover high-quality solutions and adapt the trust regions dynamically. Our work is an effective endeavor towards learning the Pareto set for multi-objective molecular design, providing decision-makers with the capability to fine-tune their preferences and thoroughly explore the Pareto set. Experimental results demonstrate that MLPS achieves state-of-the-art performance across various multi-objective scenarios, encompassing diverse objective types and varying numbers of objectives. The effectiveness of MLPS was further validated through real-world challenges in discovering antifungal peptides with low toxicity and high activity.

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YNIMG Journal 2025 Journal Article

Tasting emotions: An in-depth fmri study exploring gustatory and visual cross-modal associations across various spatio-temporal regions of the human brain

Jie Chen
Yuansheng Liu
Lina Huang
Luming Hu
Xueying Li
Liuqing Wei
Weiping Yang
Simin Zhao

This study investigates how taste influences emotional face recognition, focusing on the cross-modal interaction between gustatory and visual stimuli. While prior research has primarily examined how visual cues modulate taste perception, the reverse direction-how taste shapes visual processing in emotional contexts-remains underexplored. Using a combination of task-based functional MRI (task-fMRI) and resting-state fMRI (rs-fMRI), we examined the neural mechanisms by which taste modulates the perception of emotional faces. Behaviorally, sour tastes facilitated faster recognition of disgusted faces, while sweet tastes enhanced the detection of pleasant expressions. Neuroimaging results revealed that these emotionally congruent taste-face pairings elicited distinct activation patterns in the early visual cortex, including a significant interaction effect in the right calcarine gyrus (primary visual cortex, V1). Task-fMRI also showed modulation in the medial cingulate gyrus, fusiform gyrus, and superior frontal regions depending on emotional congruency. Resting-state fMRI revealed sustained alterations in intrinsic connectivity within the medial cingulate and paracingulate cortex following cross-modal dissonance, suggesting lasting neural effects beyond stimulus presentation. Together, these findings demonstrate the dynamic and enduring influence of taste on emotional face processing and offer novel insights into the neural basis of multisensory affective integration. By integrating task-based and resting-state fMRI, this study provides a comprehensive framework for understanding how affectively salient gustatory inputs shape social perception through both early perceptual and sustained neural mechanisms.

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TCS Journal 2024 Journal Article

Dynamic threshold spiking neural P systems with weights and multiple channels

Yanyan Li
Bosheng Song
Yuansheng Liu
Xiangxiang Zeng
Shengye Huang

Membrane computing represents a sophisticated branch of computational science that assimilates the characteristics of cellular membranes and harnesses mathematical principles. It derives inspiration from the intricate structure and functional attributes exhibited by biological cell membranes. This exposition is dedicated to an in-depth exploration of spiking neural P systems (SN P systems), meticulously crafted to emulate the intricate signaling and interaction phenomena between cellular entities. Nevertheless, conventional spiking neural P systems confront inherent constraints pertaining to their excitation rules, which hinder their applicability to real-world challenges. In pursuit of overcoming these limitations, we introduce a novel paradigm encompassing the dynamic thresholds, synaptic weights, and the integration of multiple channels within synapses. This innovative framework culminates in the dynamic threshold spiking neural P systems with weights of synapses and multiple channels in synapses (DSNP-WM systems). It is noteworthy that DSNP-WM systems demonstrate Turing universality, effectively functioning as versatile entities capable of both number generation and acceptance, in addition to performing intricate computations. Importantly, these systems showcase the efficiency in tackling challenges posed by semi-uniform solutions, exemplified by the Subsets Sum problem—a fundamental member of the NP-complete problem class, which employs a nondeterministic approach.

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JBHI Journal 2024 Journal Article

PEB-DDI: A Task-Specific Dual-View Substructural Learning Framework for Drug–Drug Interaction Prediction

Xiangzhen Shen
Zimeng Li
Yuansheng Liu
Bosheng Song
Xiangxiang Zeng

Adverse drug-drug interactions (DDIs) pose potential risks in polypharmacy due to unknown physicochemical incompatibilities between co-administered drugs. Recent studies have utilized multi-layer graph neural network architectures to model hierarchical molecular substructures of drugs, achieving excellent DDI prediction performance. While extant substructural frameworks effectively encode interactions from atom-level features, they overlook valuable chemical bond representations within molecular graphs. More critically, given the multifaceted nature of DDI prediction tasks involving both known and novel drug combinations, previous methods lack tailored strategies to address these distinct scenarios. The resulting lack of adaptability impedes further improvements to model performance. To tackle these challenges, we propose PEB-DDI, a DDI prediction learning framework with enhanced substructure extraction. First, the information of chemical bonds is integrated and synchronously updated with the atomic nodes. Then, different dual-view strategies are selected based on whether novel drugs are present in the prediction task. Particularly, we constructed Molecular fingerprint–Molecular graph view for transductive task, and Bipartite graph–Molecular graph view for inductive task. Rigorous evaluations on benchmark datasets underscore PEB-DDI's superior performance. Notably, on DrugBank, it achieves an outstanding accuracy rate of 98. 18% when predicting previously unknown interactions among approved drugs. Even when faced with novel drugs, PEB-DDI consistently exhibits outstanding generalization capabilities with an accuracy rate of 88. 06%, attributing to the proper migrating of molecular basic structure learning.

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AIIM Journal 2024 Journal Article

SSR-DTA: Substructure-aware multi-layer graph neural networks for drug–target binding affinity prediction

Yuansheng Liu
Xinyan Xia
Yongshun Gong
Bosheng Song
Xiangxiang Zeng

Accurate prediction of drug–target binding affinity (DTA) is essential in the field of drug discovery. Recently, scientists have been attempting to utilize artificial intelligence prediction to screen out a significant number of ineffective compounds, thereby mitigating labor and financial losses. While graph neural networks (GNNs) have been applied to DTA, existing GNNs have limitations in effectively extracting substructural features across various sizes. Functional groups play a crucial role in modulating molecular properties, but existing GNNs struggle with feature extraction from certain motifs due to scale mismatches. Additionally, sequence-based models for target proteins lack the integration of structural information. To address these limitations, we present SSR-DTA, a multi-layer graph network capable of adapting to diverse structural sizes, which can extract richer biological features, thereby improving the robustness and accuracy of predictions. Multi-layer GNNs enable the capture of molecular motifs across different scales, ranging from atomic to macrocyclic motifs. Furthermore, we introduce BiGNN to simultaneously learn sequence and structural information. Sequence information corresponds to the primary structure of proteins, while graph information represents the tertiary structure. BiGNN assimilates richer information compared to sequence-based methods while mitigating the impact of errors from predicted structures, resulting in more accurate predictions. Through rigorous experimental evaluations conducted on four benchmark datasets, we demonstrate the superiority of SSR-DTA over state-of-the-art models. Particularly, in comparison to state-of-the-art models, SSR-DTA demonstrates an impressive 20% reduction in mean squared error on the Davis dataset and a 5% reduction on the KIBA dataset, underscoring its potential as a valuable tool for advancing DTA prediction.

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IJCAI Conference 2023 Conference Paper

GPMO: Gradient Perturbation-Based Contrastive Learning for Molecule Optimization

Xixi Yang
Li Fu
Yafeng Deng
Yuansheng Liu
Dongsheng Cao
Xiangxiang Zeng

Optimizing molecules with desired properties is a crucial step in de novo drug design. While translation-based methods have achieved initial success, they continue to face the challenge of the “exposure bias” problem. The challenge of preventing the “exposure bias” problem of molecule optimization lies in the need for both positive and negative molecules of contrastive learning. That is because generating positive molecules through data augmentation requires domain-specific knowledge, and randomly sampled negative molecules are easily distinguished from the real molecules. Hence, in this work, we propose a molecule optimization method called GPMO, which leverages a gradient perturbation-based contrastive learning method to prevent the “exposure bias” problem in translation-based molecule optimization. With the assistance of positive and negative molecules, GPMO is able to effectively handle both real and artificial molecules. GPMO is a molecule optimization method that is conditioned on matched molecule pairs for drug discovery. Our empirical studies show that GPMO outperforms the state-of-the- art molecule optimization methods. Furthermore, the negative and positive perturbations improve the robustness of GPMO.

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