口頭発表／ポスター発表
<English>
| 発表！ | 口頭発表賞 ポスター賞・Like! Poster Award |
| ▶口頭発表 演題一覧 | ▼ポスター発表 演題一覧 ・発表日時をご確認ください。 ・各No.をクリックするとabstractが開きます。 |
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| ＜発表時間＞ Presentation time (Duty time) | |
| (A) October 28 (Tue) 16:00-17:00 & October 29 (Wed) 17:00-18:00 |
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| (B) October 28 (Tue) 17:00-18:00 & October 29 (Wed) 16:00-17:00 | |
| <カテゴリー＞ | |||
| P01 計算化学（分子計算・分子認識） | P05 バイオインフォマティクス | P09 健康科学 | |
| P02 データサイエンス | P06 創薬応用 | P10 AI創薬 | |
| P03 量子構造生命科学 | P07 臨床インフォマティクス | P11 その他 | |
| P04 ADME・毒性 | P08 分子ロボティクス | ||
* Apply for the "Like! Poster Award"
| Poster No. | Name | Affiliation | Title | Duty time |
| P01 計算化学（分子計算・分子認識） Computational Chemistry (Molecular Modeling・Molecular Recognition) | ||||
| P01-01* | Seita Kawakami | Kagoshima University | A Study on the Improvement of an Antibody Epitope Prediction Method Using Protein-Protein Docking | (A) |
| P01-02* | Sakura Hyakuta | Kagoshima University | Development of a Method for the Rational Design of Chemical Chaperones and Its Application to Anti-Prion Compounds | (B) |
| P01-03* | Ayato Mizuno | Graduate school of Pharmacy, Meijo University | Virtual Alanine Scan for Predicting Drug Resistance to SARS-CoV-2 Main Protease Inhibitors | (A) |
| P01-04* | Chie Motono | National Institute of Advanced Industrial Science and Technology (AIST) | CrypTothML: A Hybrid MD–ML Approach for Cryptic Site Prediction | (B) |
| P01-05 | Takashi Yoshidome | Department of Applied Physics, Graduate School of Engineering, Tohoku University | Deep-learning Model for Fast Computation of Grid Inhomogeneous Solvation Theory | (A) |
| P01-06 | Masatake Sugita | Institute of Science Tokyo | Analysis of membrane permeation processes of cyclic peptides on multiple reaction coordinates based on the Markov state model | (B) |
| P01-07* | Taketo Tsuga | The University of Osaka | Enhancing Kinase Substrate Specificity Prediction by Integrating Structural and Dynamic Interaction Features | (A) |
| P01-08* | Takanori Aoki | PeptiDream Inc. | Rotamer Profiling of Non-Canonical Amino Acids for Enhanced Ramachandran Mapping | (B) |
| P01-09* | Mana Uwatoko | Yokohama City University | Machine Learning Assessment of TCR–pHLA Interactions Using AlphaFold-Based Structural Models | (A) |
| P01-10* | Hajime Sugiyama | Mitsubishi Chemical Corporation | In Silico Insight into the Structural Basis of Allosteric Inhibitor Selectivity between ERK2 and p38α | (B) |
| P01-11* | Junyi Yu | The University of Osaka | Protein-protein coupled intrinsic dynamics webtool development and application | (A) |
| P01-12 | Tatsuya Ohyama | Institute of Physical and Chemical Research | The effect of packing on GTP hydrolysis of Ras in crystal | (B) |
| P01-13* | Teppei Yamada | Okayama University | Asymmetry and Heterogeneity in the Plasma Membrane | (A) |
| P01-14* | Hiroaki Oheda | yokohama-city University | Collective behavior of the Type 51 R-body predicted by AlphaFold 3 | (B) |
| P01-15* | Hikaru Higuchi | Meiji University | Structure and diffusivity of water coexisting with antithrombogenic polymer | (A) |
| P01-16* | Matsumoto Hiromu | Kyushu University | Development of Machine Learning Force Fields for Cyclic Peptides: Generating Data with the Fragment Molecular Orbital Method to Explore Applicability | (B) |
| P01-17* | Masahiro Shimizu | Institute of Science Tokyo | Development of an automatic parameter adjustment method for REST/REUS MD and its application to predicting the membrane permeability of cyclic peptides | (A) |
| P01-18* | Yuta Kawaura | Kyushu University | Predicting Protein-Ligand Binding Affinity via Markov State Modeling and Fragment Molecular Orbital Analysis | (B) |
| P01-19 | Yoshiki Yugami | Osaka Metropolitan University Graduate School of Science | Experiment-based Structural Ensemble Construction of Linear Diubiquitin Using Multi-scale Simulation and Deep Generative Modeling | (A) |
| P01-20* | Kenta Omoto | Grad.Sch.Sci., Osaka Metropolitan Univ. | Structural dynamics analysis of actin filament formation using molecular dynamics simulation. | (B) |
| P01-21 | Shinji Amari | MOLSIS Inc. | Development of PLIF Analysis Method Incorporating PIEDA Components from the Fragment Molecular Orbital (FMO) Method | (A) |
| P01-22* | JIN CHUAN | Institute of Science Tokyo | Predicting cyclization efficiency of cyclic tetrapeptides via molecular dynamics simulations | (B) |
| P01-23* | Noritaka Inoue | Schrödinger K.K. | Metadynamics-based Approach to Predicting the Membrane Permeability of Drug-like Compounds | (A) |
| P01-24* | Remii Takahashi | Yokohama City University | Inhibitor Screening of RseP and Elucidation of the molecular basis of substrate selectivity using AlphaFold3 | (B) |
| P01-25 | Takashi Amisaki | Tottori University | MM/PB(GB)SA and conformational analyses of hMTH1-nucleotide complexes | (A) |
| P01-26* | Keisuke Yanagisawa | Institute of Science Tokyo | Quantitative Estimation of Protein-Ligand Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation | (B) |
| P01-27* | Genki Kudo | University of Tsukuba | Exploring Structural Diversity of PROTAC-Mediated Ternary Complexes via Extensive Conformational Search | (A) |
| P01-28* | Junya Yamagishi | Preferred Networks | Evaluating Performance of Binding Free Energy Perturbation with NNP-driven Custom Force Field |
(B) |
| P01-29* | Hirofumi Watanabe | WithMetis Co., Ltd. | Practical preprocessing and visualization of fragment molecular orbital calculations for drug design | (A) |
| P01-30 | Masao Fujisawa | Dept. of Botechnological Science, Kindai University | Interaction of cyclic peptide drug with beta-Cyclodextrin | (B) |
| P01-31* | Tomohiro Sato | RIKEN | Application of the FMO prediction models to 3D protein structures predicted by AlphaFold2 and MD simulations. | (A) |
| P01-32 | Naofumi Nakayama | CONFLEX Corporation | Re-Evaluation of Protein-Peptide Binding Poses by Conformation and Orientation Search of Peptide with All-Atom Model | (B) |
| P01-33 | Takuya Fujie | Institute of Science Tokyo | log Pow Prediction for Cyclic Peptides Using Molecular Simulations | (A) |
| P01-34* | Masaki Mishina | Grad. Sch. BOST KINDAI Graduate School of Biology-Oriented Science and Technology Major in Biological Systems Engineering | Dynamics and Interaction of the Novel Anticoagulant AFS Warhead-Endowed Covalent Aptamer TBA4 with Thrombin, a Key Target in Blood Coagulation | (B) |
| P01-35* | Nanami Matsumoto | Grad. Sch. of KINDAI Univ. | Molecular dynamics simulation study of PET Tracer PBB3 and TMEM Fibril interactions | (A) |
| P01-36* | Koki Yano | Graduate school of science and technology , Keio university | Investigating the Allosteric Inhibition Mechanism of the Target Protein for Type 1 Diabetes Using Molecular Dynamics Simulation | (B) |
| P01-37 | Yuki Miyaguchi | MOLSIS Inc. | MOE Interface Development for MD Calculation Software "GENESIS" | (A) |
| P01-38* | Ryusei Kumatani | Nihon University | Structural dynamics and conformational behavior of aptamer binding to AML1 protein | (B) |
| P01-39* | Hiromitsu Shimoyama | The Noguchi Institute | Structural and Interaction Analysis for Understanding the Specificity of the O-Glycoprotease IMPa | (A) |
| »ページTOPへ | ||||
| P02 データサイエンス Data Science | ||||
| P02-01* | So Ukiyama | CHUGAI PHARMACEUTICAL CO., LTD. | Integrated Analytical Platform to Accelerate Scientist-Driven CMC Research | (B) |
| P02-02* | Koh Sakano | Institute of Science Tokyo | Natural Product-likeness Prediction with Chemical Language Models | (A) |
| P02-03* | Shota Gunji | Institute of Science Tokyo | Machine Learning-Based Discovery of Narrow-Spectrum Antibiotics | (B) |
| P02-04* | Takamasa Suzuki | Institute of Science Tokyo | Development of a de novo molecular generative model using decoupled setting in multi-objective Bayesian optimization | (A) |
| P02-05* | Chisato Hayakawa | Department of Regulatory Science, Faculty of Pharmaceutical Sciences, Nagoya City University | A Chemical Structure-Based Machine Learning Model for Semi-quantitative Prediction of Human Acetylcholinesterase Inhibitory Activity | (B) |
| P02-06* | Rintaro Yashiro | Science Tokyo | Exploring Structured Biological Pathways in Context with Retrieval-Augmented Generation | (A) |
| P02-07* | Yusuke Tateishi | Kumamoto University | Interpretable Activity Prediction of SGLT2 Inhibitors using Dynamics- and Electronic-Structure-Augmented Graph Attention Networks | (B) |
| P02-08* | Takafumi Nishii | Yokohama National University | Efficient Discovery of Ferroptosis Inhibitors in the Biphenol Space via Synthetic Feasibility Prediction using Positive-Unlabeled Machine Learning | (A) |
| P02-09* | Yuto Matsumoto | Yokohama National University | Compound Embeddings from Textual Data and Fingerprints by Doc2Vec and Classification and Interpretability Using Them | (B) |
| P02-10* | Yuki Sato | Science Tokyo | Study on Feature Extraction Models for Protein 3D Structures Using AlphaFold2-based Neural Networks | (A) |
| P02-11 | Shun Uratani | Miyashita Laboratory, Department of Modern Mechanical Engineering, Graduate School of Creative Science and Engineering, Waseda University | Sensitivity Analysis of Tumor Angiogenesis and Growth Based on 3D Computational Modeling of Cell Activity and Vital Energy | (B) |
| P02-12* | Yi-An Chen | National Institutes of Biomedical Innovation, Health and Nutrition | BAIKINMINE, a mine of integrated microbiome knowledge | (A) |
| P02-13* | Rina Hirahara | INSTITUTE for PROTEIN RESEARCH THE UNIVERSITY OF OSAKA | Construction of a machine learning model to predict compounds targeting G-quadruplexes formed by ALS/FTD-related C9orf72 hexanucleotide repeats | (B) |
| P02-14* | Yixuan Sui | Keio University | Enhancing the Predictive Performance of PPI Inhibitory Activity Models through Data Imbalance Correction | (A) |
| P02-15 | Kikuko Kamisaka | RIKEN | Recent Developments of FMODB in 2025: Enhancing FMO Data Accessibility through Visualization Tools | (B) |
| P02-16* | Nobuaki Yasuo | Science Tokyo | TRACER: reaction-based molecular optimization using conditional transformer and Monte-Carlo tree search | (A) |
| P02-17* | Sosuke Asano | Graduate School, Keio University | An Unsupervised Deep Learning Method to Identify Charasteristic Amino Acid Residues from Molecular Dynamics Simulation by Comparing Similar Systems | (B) |
| P02-18* | Tsubasa Nagae | Yokohama City University | Development of an Integrated Machine Learning Model for the Design and Prediction of PPI Modulators | (A) |
| P02-19* | Kenta Sumitomo | The university of tokyo | Prediction of antibody non-specificity and identification of antibody candidates using machine learning with NGS data from selection experiments | (B) |
| P02-20* | Takuho Ri | The University of Tokyo | Compressing the Uncurated PubChem-120M: A Universal Chemical Latent Space trained on SMILES denoising | (A) |
| P02-21* | Yoshinobu Igarashi | RIKEN | Toward Multimodal Foundation Models: Assessment of Encoders for Compounds, Sequences, Expression, and Language | (B) |
| P02-22* | Seiji Matsuoka | RIKEN | Implementation of a Chemical Structure Database System Bridging Open Science and Drug Discovery | (A) |
| »ページTOPへ | ||||
| P03 量子構造生命科学 Quantum-Structural Life Science | ||||
| P03-01* | Mayu Kitano | Osaka Metropolitan University | Crystal structures of the staurosporine complexes provide a basis for developing highly selective MAP2K4 and MAP2K6 inhibitors. | (B) |
| P03-02* | Yusuke Takashima | National Institutes of Biomedical Innovation, Health and Nutrition | Refinement of RNA Tertiary Structures via Distance‑Map Correction and Machine Learning | (A) |
| P03-03* | Keiichi Kimura | AOI Biosciences Inc. | Novel Allosteric Drug Discovery Platform based on Quantum-Inspired Optimization Solutions “SQBM+” | (B) |
| P03-04* | Shuhei Miyakawa | The University of Osaka | Can Fragment Molecular Orbital Calculations Explain Ligand Binding Characteristics? A Comprehensive Study Using PDB from BindingDB | (A) |
| P03-05* | Ryoya Kawabata | Osaka Metropolitan University | Electron Density Topography (EDT) Based on Middle-Angle X-ray Scattering Data Reveals Novel Structural Insights into MAP2K6 and p38α MAPK in solutions. | (B) |
| »ページTOPへ | ||||
| P04 ADME・毒性 ADMET | ||||
| P04-01* | Motohiro Kato | DMPK academy | Top-down approach for prediction of drug-drug interactions using physiologically based pharmacokinetic model | (A) |
| P04-02* | Kiyoshi Hasegawa | TECHNOPRO R&D company | New Molecular Design Using PBPK Simulations and Machine Learning Models | (B) |
| P04-03* | Koji Jojima | National Institute of Health Sciences | Evaluating Pre-trained Transformer Models for Toxicity Prediction Task: Effectiveness and Performance for Hepatotoxicity. | (A) |
| P04-04* | Takuya Suzuoka | Institute of Science Tokyo | Integrating Pharmacokinetic Principles into Deep Learning for Reliable ADMET Profiling | (B) |
| P04-05* | Yohei Ohto | Graduate School of Pharmaceutical Sciences, The University of Tokyo | Validity and application of temporal information extracted from patent information | (A) |
| P04-06* | Kotaro Suzuki | Graduate School of Pharmaceutical Sciences, Nagoya City University | In Silico Prediction of Gapmer Antisense Oligonucleotides-induced ALT Elevation | (B) |
| P04-07 | Tsuyoshi Kato | Gunma University | Biology-Driven Gene Selection Improves RNA-Seq–Based Toxicity Prediction in StemPanTox Beta | (A) |
| P04-08* | Ryoko Terada | Institute for Protein Research, Osaka University | Application of Km and Vmax-Based Prediction Models to Assessing the Influence of CYP2C9 Polymorphisms on Pharmacokinetics | (B) |
| P04-09* | Soyoka Tanihata | Tottori University | Modeling Ethnic Differences in Drug Clearance via Predicted Pharmacokinetics from Chemical Structures | (A) |
| P04-10* | Ayane Takamatsu | Institute for Protein Research, The University of Osaka | Predicting substrates for transporters involved in the drug transfer into breast milk | (B) |
| P04-11 | Tomoya Aoyagi | Department of Chemistry and Biochemistry, Graduate School of Advanced Science and Engineering, Waseda University | Metabolomic Analysis Driven Search for Biologically Active Marine Natural Products from the Mixture of Dredge Bycatch | (A) |
| »ページTOPへ | ||||
| P05 バイオインフォマティクス Bioinformatics | ||||
| P05-01* | Sachiko Kawano | POLA CHEMICAL INDUSTRIES, INC. | XGBoost–Based Gene Expression Profiling of Senile lentigo Using Skin Transcriptomic Data Collected by Microbiopsy | (B) |
| P05-02 | Chiaki Handa | Kissei pharmaceutical. Co., Ltd. | Bayesian Network Analysis for Disease Mechanism Estimation and Drug Target Discovery | (A) |
| P05-03* | Yajie Hu | Osaka University, Institute for Protein Research (IPR) | Comparing Structural and Dynamic Differences Among Globin-like Proteins | (B) |
| P05-04* | Kouki Maebara | Nagoya City University | Development of a Predictive Model for Chemically Induced Rat Liver Cell Necrosis Using Visualized Transcriptome Data | (A) |
| P05-05* | Kodai Miyazaki | School of Pharmaceutical Sciences, University of Shizuoka | Identification of Cancer-Associated Fibroblast Subtypes That Promote HCC Progression and Their Differentiation Programs | (B) |
| P05-06* | Yulong Gou | Institute for Protein Research, The University of Osaka | A Mechanism-based Prediction Model of P-glycoprotein Compound Efflux | (A) |
| P05-07* | Akira Shinohara | Department of Computer Science, School of Computing, Institute of Science Tokyo | Compound Retrosynthesis Analysis Using Consensus Estimate | (B) |
| P05-08* | Wen Tao Wu | Osaka University IPR | Machine Learning-Guided Design of Thermostable Proteins: Leveraging Bayesian Optimization for Efficient Mutational Scanning | (A) |
| »ページTOPへ | ||||
| P06 創薬応用 Drug Discovery Application | ||||
| P06-01* | Akimi Hori | JAPAN TOBACCO INC. | Building a system to support a data-driven drug discovery DMTA cycle | (B) |
| P06-02* | Seisuke Takimoto | JAPAN TOBACCO INC. | Initiatives for predict-first DMTA cycle in JT | (A) |
| P06-03* | Kairi Furui | Institute of Science Tokyo | ALLM-Ab: Active Learning-Driven Antibody Optimization Using Fine-tuned Protein Language Models | (B) |
| P06-04* | Apakorn Kengkanna | Institute of Science Tokyo | CatDRX: Reaction-Conditioned Generative Model for Catalyst Design and Optimization | (A) |
| P06-05* | Sho Masunaga | Institute of Science Tokyo | GraphBioisostere: General Bioisostere Prediction Model with Deep Graph Neural Network |
(B) |
| P06-06* | Masami Sako | Institute of Science Tokyo | DiffPharma : A Conditional Diffusion Framework for Interaction-Constrained 3D Molecular Design | (A) |
| P06-07* | Yuta Kikuchi | Institute of Science Tokyo | Binding Interaction Analysis of Anticancer Saponin OSW-1 with Oxysterol-binding Proteins | (B) |
| P06-08 | Masataka Kuroda | National Institutes of Biomedical Innovation, Health and Nutrition | Analysis of hydration related to double-strand stability of nucleic acid medicines | (A) |
| P06-09 | Hiroto Terada | Grad. Sch. Sci., Osaka Metropolitan Univ. | Discovery of novel inhibitor candidate compounds using accurate in silico screening protocol | (B) |
| P06-10* | Yuki Murakami | Yokohama City University | Data-driven Design of PROTAC Linkers to enhance Cell Membrane Permeability | (A) |
| P06-11* | Yunoshin Tamura | Preferred Networks, Inc. | Application of Relative Binding Free Energy Perturbation (RBFEP) to Multiple Compounds Bound to One Binding Site Simultaneously | (B) |
| P06-12 | Kohei Ohta | Medical and Biological Labratory Co., Ltd. | Design and Optimization of Anti-FGFR4 Minibinders by Integrating Machine Learning and Computational Chemistry | (A) |
| P06-13* | Masayoshi Shimizu | Institute of Science Tokyo | COFFEE-PRESC: a fast pre-screening method using chemical compound retrieval by fragment pose pairs | (B) |
| P06-14* | Mio Yokoyama | Kumamoto University | Discovery and Optimization of Natural Product-Derived GLS1 Inhibitors via Quantum Chemical Analysis | (A) |
| P06-15* | Satoshi Yoneyama | Institute of Science Tokyo | Construction of representative fragment sets based on mutual 3D structural similarity and docking feasibility for fragment-based virtual screening | (B) |
| P06-16* | Kaho Akaki | Institute of Science Tokyo | Enhancing virtual screening accuracy by refining docking calculation scoring with mixed-solvent molecular dynamics | (A) |
| P06-17* | Masahito Ohue | Institute of Science Tokyo | Computational Design of Monoclonal Antibodies Using Protein Language Models, Structure Prediction, and Physics-Based Evaluation: Application to Human TIGIT Targeting | (B) |
| P06-18* | Asato Yamauchi | Institute of Science Tokyo | Integrating Antibody and Payload Information for Predicting the Drug-to-Antibody Ratio of Antibody-Drug Conjugates via Machine Learning | (A) |
| P06-19* | Ryoya Nakano | Institute of Science Tokyo | Improvement of fragment-based protein–ligand docking using the Quantum Annealer | (B) |
| P06-20* | Chiharu Konda | OpenEye, Cadence Molecular Sciences | Predicting affinity: 3D QSAR and its interplay with RBFE by NES | (A) |
| P06-21* | Kei Sato | Department of Chemistry and Biochemistry, Graduate School of Advanced Science and Engineering, Waseda University | LC/MS-based metabolomic analysis of marine sponge species of genus Petrosia and the identification of a new polyacetylene | (B) |
| P06-22* | Yusuke Ihara | Ajinomoto Co., Inc. | Development of a Novel 3D Molecular Representation for Odorants: Toward Predictive Modeling of Olfactory Receptor Activity and Odor Perception | (A) |
| P06-23 | Kotaro Osaki | Division of Gastrointestinal and Pediatric Surgery, Department of Surgery, School of Medicine, Tottori University Faculty of Medicine | Evaluation of a Docking-Based Prediction Method from Apo Structures Using CDK2 Inhibitors | (B) |
| »ページTOPへ | ||||
| P07 臨床インフォマティクス Clinical Application | ||||
| P07-01 | Taro Oshiro | JAPAN TOBACCO INC. | Predicting the biological pathways activated by cigarette or heated tobacco product use: a proof-of-concept study | (A) |
| P07-02* | Genki Masuda | Institute of Science Tokyo | Computational Identification of Antigen-Specific Sequences from BCR Repertoires Using an Antibody Language Model | (B) |
| P07-03* | Hayato Nakahara | Tottori university | Development and Evaluation of a Machine Learning Model for Classifying Neurodegenerative Diseases from Transcriptomic and GO Data | (A) |
| »ページTOPへ | ||||
| P08 分子ロボティクス Molecular Robotics | ||||
| P08-01* | HISASHI TADAKUMA | ShanghaiTech University | Development of DNA origami nanodevices to capture and analyze expressome | (B) |
| P08-02* | Shogo Kinugawa | Department of Applied Chemistry, Graduate School of Engineering, Mie University | Shape-transformable DNA origami tubes for programmable stacking-mediated self-assembly | (A) |
| P08-03* | Reo Toho | Department of Applied Chemistry, Graduate School of Engineering, Mie University | DNA Origami Nanoactuators for Stimulus-Responsive and Programmable Liposome Shape Control | (B) |
| P08-04* | ** Canceled ** | |||
| P08-05 | Shin-ichiro Nomura | Graduate school of Engineering, Tohoku University | Development of Multicellular-Type Molecular Robots with Nucleic Acid Sensors for MPS Applications | (B) |
| P08-06* | Ren Nobusawa | Graduate School of Medical Life Science, Yokohama City University | Development of a Soft Robotics-Based Physical Simulator Reproducing the Motion Mechanism of V1-ATPase | (A) |
| P08-07* | Seiichi Ishida | Sojo University | Advancing Microphysiological Systems for Non-Animal Drug Testing: Trends in Technical Considerations and their Solutions with Molecular Robotic Technologies | (B) |
| P09 健康科学 Health Sciences | ||||
| P09-01* | Yuai Fukuzawa | Institute of Science Tokyo | Microbiome as biomarkers of ICI in esophageal cancer patients | (A) |
| »ページTOPへ | ||||
| P10 AI創薬 AI Drug Discovery | ||||
| P10-01* | Kosuke Takeuchi | DAIICHI SANKYO CO., LTD. | Development and Enhancement of NITER: Expanding Access to Multi-Billion-Scale Compound Libraries and Public Data Sources | (B) |
| P10-02* | Akitoshi Okada | Daiichi Sankyo Co., Ltd. | Boltz2 benchmark on in-house dataset: thinking of how to effectively use in drug discovery campaigns | (A) |
| P10-03* | Reiji Teramoto | Chugai pharmaceutical, Co., Ltd. | Enhancing ADME Property Prediction with Ensemble C-Mixup TabPFN in a low data regime. | (B) |
| P10-04* | Yasunobu Yamashita | The University of Osaka | Deep Learning-Aided Drug Discovery via the Latent Space Visualization of Deep Neural Networks | (A) |
| P10-05* | Calvin Davey | TechnoPro, Inc. TechnoPro R&D Company | Assessing Generative AI Embeddings for Predicting Drug Response from scRNA-Seq | (B) |
| P10-06* | Shogo Nakamura | Institute of Science Tokyo | Learning Chemical Reaction Trajectories with Transformer and GFlowNet for Molecular Optimization | (A) |
| P10-07* | Koshiro Aoki | Institute of Science Tokyo | Contrastive Learning on Protein Binding Structures for Drug-Target Interaction Prediction | (B) |
| P10-08* | Victoire Cachoux | Iktos K.K. | Synthesis-driven GenAI for Molecule Design: Growing and Linking Optimizers | (A) |
| P10-09* | Yiming Zhang | Graduate School of Frontier Sciences, The University of Tokyo | Leveraging LLM and Bayesian Optimization for Multi-Objective Lead Optimization | (B) |
| P10-10* | Tomoya Nabetani | Yokohama city university | Enhancing Protein-Protein Interaction Affinity Prediction with TabPFN and Rosetta-Based Structural Descriptors | (A) |
| P10-11* | Ryo Ogawa | Institute of Science Tokyo | Quantum-informed AI for drug discovery: enhancing generalizability in compound-protein interaction prediction | (B) |
| P10-12* | Shinya Kawano | Gifu pharmaceutical university | Evaluating Deep Learning Predictions and Score Integration for Drug Discovery | (A) |
| P10-13* | Taichi Ishikawa | Institute of Science Tokyo | Predicting Protein Allosteric Site based on Atomistic Energy-Weighted Graphs | (B) |
| P10-14* | Shota Takahashi | Mitsui Knowledge Industry | QAEmap: A deep learning-based method for evaluating ligand coordinate validity in protein-ligand complex structures | (A) |
| P10-15* | Takashi MATSUMOTO | Institute of Science Tokyo | Conditional Molecular Generation Using 3D Pocket and Interaction Features | (B) |
| P10-16* | Haris Hasic | Elix, Inc. | kMoL: An Open-source Machine and Federated Learning Library for Drug Discovery | (A) |
| P10-17* | Taiyo Toita | Graduate School of Medical Life Science, Yokohama City University | Activity prediction-driven optimization of a V-ATPase inhibitor using molecular generative AI | (B) |
| P10-18 | Kohtaro Yuta | In Silico Data,Ltd. | Challenges in Integrating Chemistry and AI for Drug Development | (A) |
| P10-19* | Tatsuya Yoshizawa | Graduate School of Medical Life Science, Yokohama City University | Molecule Generation with Boltz-2: A Case Study on Kinase Inhibitor Design | (B) |
| P10-20 | Jinzhe Zhang | Preferred Networks Inc | ML-Boosted Virtual Screening at Billion-Compound Scale with Uni-Dock | (A) |
| P10-21* | Yuki Satoh | ONO PHARMACEUTICAL CO., LTD | REINVENT4 Ecosystem and LLM-Powered Patent Analysis Tool: OSS Implementation with Case Studies | (B) |
| P10-22* | Takuto Koyama | Graduate School of Medicine, Kyoto University | Empowering Federated Learning for Robust Compound-Protein Interaction Prediction across Heterogeneous Cross-Pharma Domains | (A) |
| P10-23* | Yasuhiro Yoshikai | The University of Tokyo Graduate School of Pharmaceutical Sciences | Evaluating Mamba as a backbone for language-based foundation models for ligand generation | (B) |
| P11 その他 Others | ||||
| P11-01* | Miho Irie | Cross-Industrial Data Science Labs | Buildig a Pipeline for Designing Novel Drug Candidates using Quantum Annealing | (A) |
| P11-02* | Kimiko Kitamura | National Institute of Health Sciences | The performance characteristics of the commercially available blood brain barrier (BBB)-model installing human induced pluripotent stem cell (hiPSC)-derived BBB cells | (B) |