| Poster No. |
Name |
Affiliation |
Title |
Duty
time |
| P01 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) |
| »Top of this page |
| P02 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) |
| »Top of this page |
| P03 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) |
| »Top of this page |
| P04 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) |
| »Top of this page |
| P05 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) |
| »Top of this page |
| P06 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) |
| »Top of this page |
| P07 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) |
| »Top of this page |
| P08 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 Health Sciences |
| P09-01* |
Yuai Fukuzawa |
Institute of Science Tokyo |
Microbiome as biomarkers of ICI in esophageal cancer patients |
(A) |
| »Top of this page |
| P10 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 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) |
