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Tetsuya Sakurai

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7 papers

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JBHI Journal 2026 Journal Article

A Dual-Language-Model Framework for Reproducibility in Small Molecule-RNA Binding Site Prediction

Shixuan Guan
Xiucai Ye
Tetsuya Sakurai

Single-seed evaluation—the dominant reporting practice in small-dataset molecular learning—can substantially inflate performance estimates yet remains largely unexamined. We present the first systematic reproducibility analysis for RNA–ligand binding site prediction by integrating two large pretrained RNA language models (RNA-FM and RiNALMo) across multiple fusion architectures and replicated training runs on the TR60/TE18 benchmark. Our analysis reveals a pronounced Peak–SOTA Paradox: a favorable initialization in the Reverse Cross-Attention model reached an MCC of 0. 353, surpassing the reported state-of-the-art (0. 327), whereas multi-seed replication yielded only 0. 266 $\pm$ 0. 020—a 32. 8% overestimation. Across architectures, mean accuracy remained tightly clustered, yet reproducibility varied substantially. Simple concat fusion strategies exhibited markedly higher stability than attention-based models, indicating that architectural entanglement rather than parameter count governs variance under data scarcity. Collectively, these findings establish reproducibility as a primary evaluation criterion for small-sample molecular prediction and motivate a dual-reporting standard in which mean $\pm$ SD serves as the principal metric and peak scores as supplementary evidence. This variance-aware perspective highlights that single-seed evaluations can misrepresent expected performance by 20–30% in limited-sample regimes.

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JBHI Journal 2026 Journal Article

LLM-Enhanced Knowledge Distillation for Sequence-Based Protein-Ligand Interaction Prediction

Wenyu Xi
Ruheng Wang
Xiucai Ye
Tetsuya Sakurai
Leyi Wei

Accurate prediction of protein-ligand interactions is essential for drug discovery, supporting critical stages from lead optimization to therapeutic development. Many existing methods depend on high-resolution protein-ligand complex structures, which limits scalability and reduces robustness in structure-limited settings. To address these challenges, we introduce Multi-Combinatorial Knowledge Distillation (MCKD), a sequence-based framework that predicts protein-ligand interactions without requiring explicit three-dimensional structures at inference time. MCKD represents proteins and ligands as two-dimensional molecular graphs derived from their sequences and physicochemical properties, enabling effective learning from readily available inputs. To incorporate structural knowledge beyond sequence information, MCKD employs a hybrid distillation strategy that combines cross-modal distillation from a structure-based teacher with self-distillation to improve representation consistency across layers. To model protein-ligand interactions explicitly, MCKD integrates a bilinear attention network that captures residue-atom level associations and supports both binding affinity regression and binary interaction classification. Evaluations on multiple public benchmark datasets show that MCKD consistently outperforms existing sequence-based methods and achieves performance comparable to structure-based approaches. The model also generalizes well to unseen proteins and novel ligand scaffolds, while providing interpretable insights into key molecular interaction regions. These results suggest that MCKD offers a scalable and effective solution for protein-ligand interaction prediction, particularly for structure-free and data-limited drug discovery applications.

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JBHI Journal 2025 Journal Article

PKAN: Leveraging Kolmogorov–Arnold Networks and Multi-Modal Learning for Peptide Prediction With Advanced Language Models

Li Wang
Xiangzheng Fu
Xiucai Ye
Tetsuya Sakurai
Xiangxiang Zeng
Yiping Liu

Peptides can offer highly specific biological activities, serving as essential mediators of intercellular signaling, which are critical for advancing precision medicine and drug development. Their primary structure can be depicted either as an amino acid sequence or as a chemical molecules consisting of atoms and chemical bonds. Large language models (LLMs) hold the potential to thoroughly elucidate the intricate intrinsic properties of peptides. Here we present the Peptide Kolmogorov-Arnold Network (PKAN), a framework leveraging multi-modal representations inspired by advanced language models for peptide activity and functionality prediction. Comparative experiments across tasks show that PKAN outperforms state-of-the-art models while maintaining a streamlined design with superior predictive capabilities. The multi-modal feature importance scoring, anchored in global structures and the significant marginal impacts of derived features on the model, coupled with intricate symbolic regression of specific activation functions, further demonstrates the robustness and precision of the PKAN framework in identifying and elucidating key determinants of peptide functionality. This work provides scientific evidence for investigating the complex mechanisms of peptide materials and supports the progression of peptide language paradigms in biology.

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AAMAS Conference 2022 Conference Paper

Using Agent-Based Simulator to Assess Interventions Against COVID-19 in a Small Community Generated from Map Data

Mitsuteru Abe
Fabio Tanaka
Jair Pereira Junior
Anna Bogdanova
Tetsuya Sakurai
Claus Aranha

During the COVID-19 pandemic, governments have struggled to devise strategies to slow down the spread of the virus. This struggle happens because pandemics are complex scenarios with many unknown variables. In this context, simulated models are used to evaluate strategies for mitigating this and future pandemics. This paper proposes a simulator that analyses small communities by using real geographical data to model the road interactions and the agent’s behaviors. Our simulator consists of three different modules: Environment, Mobility, and Infection module. The environment module recreates an area based on map data, including houses, restaurants, and roads. The mobility module determines the agents’ movement in the map based on their work schedule and needs, such as eating at restaurants, doing groceries, and going to work. The infection module simulates four cases of infection: on the road, at home, at a building, and off the map. We simulate the surrounding areas of the University of Tsukuba and design three intervention strategies, comparing them to a scenario without any intervention. The interventions are: 1) PCR testing and self-isolation if positive; 2) applying lockdown measures to restaurants and barbershops 3) closing grocery stores and restaurants and providing delivery instead. For all scenarios, we observe two areas where most infection happens: hubs, where people from different occupations can meet (e. g. , restaurants), and non-hubs, where people with the same occupation meet (e. g. , offices). The simulations show that most interventions reduce the total number of infected agents by a large margin. We observed that interventions targeting hubs (2-4) did not impact the infection at non-hubs. In addition, the intervention targeting people’s behavior (1) ended up creating a cluster at the testing center.

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AAAI Conference 2019 Conference Paper

Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction

Akira Imakura
Momo Matsuda
Xiucai Ye
Tetsuya Sakurai

Dimensionality reduction methods that project highdimensional data to a low-dimensional space by matrix trace optimization are widely used for clustering and classification. The matrix trace optimization problem leads to an eigenvalue problem for a low-dimensional subspace construction, preserving certain properties of the original data. However, most of the existing methods use only a few eigenvectors to construct the low-dimensional space, which may lead to a loss of useful information for achieving successful classification. Herein, to overcome the deficiency of the information loss, we propose a novel complex moment-based supervised eigenmap including multiple eigenvectors for dimensionality reduction. Furthermore, the proposed method provides a general formulation for matrix trace optimization methods to incorporate with ridge regression, which models the linear dependency between covariate variables and univariate labels. To reduce the computational complexity, we also propose an efficient and parallel implementation of the proposed method. Numerical experiments indicate that the proposed method is competitive compared with the existing dimensionality reduction methods for the recognition performance. Additionally, the proposed method exhibits high parallel efficiency.

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IJCAI Conference 2019 Conference Paper

Distributed Collaborative Feature Selection Based on Intermediate Representation

Xiucai Ye
Hongmin Li
Akira Imakura
Tetsuya Sakurai

Feature selection is an efficient dimensionality reduction technique for artificial intelligence and machine learning. Many feature selection methods learn the data structure to select the most discriminative features for distinguishing different classes. However, the data is sometimes distributed in multiple parties and sharing the original data is difficult due to the privacy requirement. As a result, the data in one party may be lack of useful information to learn the most discriminative features. In this paper, we propose a novel distributed method which allows collaborative feature selection for multiple parties without revealing their original data. In the proposed method, each party finds the intermediate representations from the original data, and shares the intermediate representations for collaborative feature selection. Based on the shared intermediate representations, the original data from multiple parties are transformed to the same low dimensional space. The feature ranking of the original data is learned by imposing row sparsity on the transformation matrix simultaneously. Experimental results on real-world datasets demonstrate the effectiveness of the proposed method.

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IJCAI Conference 2018 Conference Paper

Spectral Feature Scaling Method for Supervised Dimensionality Reduction

Momo Matsuda
Keiichi Morikuni
Tetsuya Sakurai

Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space. However, these methods do not always give the desired results due to irregularities or uncertainties of the data. Thus, we consider aggressively modifying the scales of the features to obtain the desired classification. Using prior knowledge on the labels of partial samples to specify the Fiedler vector, we formulate an eigenvalue problem of a linear matrix pencil whose eigenvector has the feature scaling factors. The resulting factors can modify the features of entire samples to form clusters in the reduced space, according to the known labels. In this study, we propose new dimensionality reduction methods supervised using the feature scaling associated with the spectral clustering. Numerical experiments show that the proposed methods outperform well-established supervised methods for toy problems with more samples than features, and are more robust regarding clustering than existing methods. Also, the proposed methods outperform existing methods regarding classification for real-world problems with more features than samples of gene expression profiles of cancer diseases. Furthermore, the feature scaling tends to improve the clustering and classification accuracies of existing unsupervised methods, as the proportion of training data increases.

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