Overview

Understanding the essence of life requires an integrated approach that spans the vast spatiotemporal scales from atoms and molecules to entire cells. This workshop aims to establish the foundational methodology for “AI-driven multiscale simulation” that harnesses recent advances in AI technology to bridge simulations across different hierarchical levels.

Through this workshop, we seek to create a new research paradigm for achieving a truly integrated understanding of life phenomena from molecules to cells, establishing “AI-driven Multiscale Life Science”.

Key Topics

Registration

Registration is now closed. Both on-site and Zoom (online) registration have reached their deadlines. Zoom connection details will be shared with registered participants before the workshop.

Venue

8th Floor, Integrated Innovation Building (IIB), RIKEN

6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan

The nearest station is Iryo Center (Medical Center) on the Port Liner. From the station, follow the 2F pedestrian deck directly to the IIB building and enter through the 2F entrance (there is no 1F entrance).

This workshop is a hybrid event. All talks will be streamed via Zoom for remote participants. The Zoom link will be shared with registered participants before the workshop.

Lunch information: There are limited dining options near the venue. Lunch boxes will be provided for invited speakers. Other participants are encouraged to bring lunch or use nearby convenience stores. Please refer to the nearby restaurant & convenience store map (PDF) for available options around the venue.

Program

Each talk: 25 min presentation + 5 min Q&A = 30 min

Click on a talk title (▶) to view the abstract

Day 1: May 28, 2026 (Thu)

08:30 – 08:55 Registration (2F, IIB)
08:55 – 09:00 Opening Remarks — Yuji Sugita (RIKEN / University of Tokyo)
Session 1: AI/ML for Molecular Dynamics — Sampling, Kinetics & Transitions (Chair: Yasuhiro Matsunaga)
09:00 – 09:30 Pratyush Tiwary (University of Maryland)
Efficient, explainable and extrapolative AI for biophysics
AI is now everywhere in chemistry, from structure prediction to molecule generation to automated synthesis. The excitement is real, but so is the unease about what is genuinely predictive and what is closer to impressive memorization. In this talk I will take a statistical physicist's perspective and use examples from my group's work to argue for cautious, but clear, enthusiasm for AI in chemistry and allied fields. I will show how we combine generative AI with statistical mechanics to learn Boltzmann weighted ensembles from limited training data, and then extrapolate across temperature, pressure, and other thermodynamic conditions reducing the need for explicit, expensive simulations or experiments. I will highlight the breadth of these methods through applications that include prediction of protein and RNA structural ensembles, and conformation selective drug discovery efforts aimed at Alzheimer's disease and hypertension.
09:30 – 10:00 Xuhui Huang (University of Wisconsin-Madison)
Machine Learning Models for Non-Markovian Protein Dynamics
10:00 – 10:30 Chris Chipot (CNRS)
From Atoms to Pathways: A Machine Learning Perspective on Molecular Transitions
Understanding rare events in complex molecular systems requires identifying transition pathways that are both physically meaningful and committor-consistent. I will introduce a neural-network-based framework that simultaneously learns the committor function and the associated committor-consistent transition pathways, providing a unified, data-driven perspective on molecular transformations. The method, rooted in the committor time-correlation function, operates beyond the overdamped diffusive limit and distinguishes multiple competing mechanisms. Next, leveraging geometric graph neural networks built on vector perceptrons, I will show how the committor can be learned directly from atomic coordinates—bypassing the need for predefined collective variables—and yields atom-level interpretability by pinpointing the key atomic contributors to the transition. Last, I will present an iterative refinement between biased sampling and committor learning to identify dominant transition channels, enhance sampling efficiency, and enable accurate estimation of rate constants. Demonstrations on benchmark potentials and molecular systems, from peptide isomerization and folding models to chemical reactions, showcase the robustness, transferability, and ability to uncover physically grounded, of these committor-consistent descriptions of complex molecular processes.
10:30 – 11:00 Anand Srivastava (Indian Institute of Science)
Morpheus: Investigating fold switching proteins through the lens of machine learning and statistical thermodynamics
Functionally important metamorphic proteins, which can switch folds between different well-folded conformations, are now thought to be widespread. These protein sequences do not obey the classical folding dogma, and the underlying principles that drive their fold-switching behavior is poorly understood. In my talk, I will present two diverse approaches towards understanding the molecular grammar of fold switchers - one based on machine learning and other on thermodynamics principles. In the first part, I will present a new fragment-based classification algorithm from my group, named as Morpheus, which can annotate fold-switching proteins from sequence information across proteomes (https://doi.org/10.1093/bioinformatics/btaf635). Since Morpheus server takes in user defined sequences, it can also act a useful screening tool towards the de novo design and engineering of such proteins through further experimentation. However, rationale de novo design of fold switching proteins will require a thorough understanding of the molecular driving forces leading to their conformational propensities. In the second part of my talk, I will discuss our recent unpublished work (https://doi.org/10.1101/2025.10.10.679401) on understanding the thermodynamic design principles that leads to fold-switching behavior in such proteins. As a test case, we considered a recently designed fold-switching protein [John Orban and co-authors, PNAS 2023] that transitions between a 3α fold and α/β fold upon changes in temperature. We performed a detailed thermodynamic analysis on this protein using an advanced and efficient free energy all-atom molecular simulation approach. We find that while 3α fold is stabilized at low temperatures by enthalpic contributions from favorable water-water and protein-water interactions, the transition to the α/β fold at high temperatures is driven by the gain of entropy from the release of ordered water molecules surrounding the 3α conformer into the bulk. Our study elucidates the importance of thinking in terms of entropy-based design in natural systems and provides a integrated machine learning and physics-based framework that can help in the design and engineering of future synthetic and functional metamorphic proteins.
11:00 – 11:20 Coffee Break
Session 2: Protein Design, Engineering & Drug Discovery (Chair: Kei-ichi Okazaki)
11:20 – 11:50 Juyong Lee (Seoul National University)
Integrating AI and Physics-Based Biomolecular Modeling for Multi-Modality Drug Discovery
The integration of artificial intelligence (AI) and molecular dynamics (MD) simulations is rapidly transforming the drug discovery process. Recent advancements demonstrate that state-of-the-art computational techniques are accelerating the discovery of drug candidates across various modalities, including small molecules, peptides, and antibodies. This presentation will explore recent progress in generative biomolecular modeling AI models applied to drug screening and candidate generation, with several case studies illustrating their practical applications. First, the new generative AI methods for protein design, protein sequence generation methods. The new methods show superior performance to existing inverse folding methods, especially for antibody CDR loop generation. Second, we will discuss the development of novel peptides for GLP1R activation, which is a target for treating obesity. Lastly, we will discuss the development of a novel artificial antigen-based RSV vaccine candidate using protein generative models. In all three cases, the success rate for identifying novel candidates was significantly higher compared to traditional high-throughput screening approaches, underscoring the practical advantages of AI-driven strategies in modern drug discovery.
11:50 – 12:20 Takahiro Kosugi (Kanazawa University)
Computational Protein Design for Rational Control of ATPases
ATP hydrolysis plays key roles in the functions of numerous proteins, including molecular motors and kinases. Therefore, the ability to control ATP hydrolysis and the resulting ATPase-dependent functions would not only deepen our understanding of biological systems but also enable their targeted manipulation. Recent advances in computational protein design have been substantial, and we recently achieved the de novo design of an ATPase (T. Kosugi, et al. Protein Sci. 2025). Building on this technology, we also introduced artificial allosteric sites into a rotary molecular motor, resulting in enhanced rotational speed (T. Kosugi, et al. Nat. Chem. 2023).
12:20 – 12:50 Jonathan Essex (University of Southampton)
Engineering antibody structure and function by multiscale simulations
Antibodies are becoming increasingly important as therapeutics, with roughly one third of drugs approved by the FDA in 2024 being biologics. Being able to rationally engineer antibodies to control their function would clearly be a powerful tool. In this presentation, I describe the combination of multiscale simulation with low-resolution small-angle X-ray scattering, together with corroborating crystal structure and assay data, to rationally engineer antibodies to deliver a controlled immunostimulatory response. I will show that crystal structures often poorly reflect solution phase behaviour, and that in these particular antibody systems, protein dynamics has a critical role in determining biological function.
12:50 – 14:10 Lunch
Session 3: Membrane Proteins, Ion Channels & Molecular Machines (Chair: Takahiro Kosugi)
14:10 – 14:40 Syma Khalid (University of Oxford)
Multiscale simulations of the E. coli outer membrane
The outer membrane of E. coli a proteolipid bilayer - new details of its spatial organisation are emerging from molecular imaging, chemical biology and molecular simulations.

We use multiscale molecular dynamics simulations to explore these details at a resolution that is not available to experimental methods. A largest, coarse-grained simulations are composed of over 10 million particles. Fine grained resolution is added with smaller simulations at all atom resolution. We also explore the impact of antimicrobial agents on these membranes.

14:40 – 15:10 Yi Wang (Chinese University of Hong Kong)
Structure of the Transmembrane Channel from the Vacuolating Toxin of Helicobacter pylori
Helicobacter pylori (H. pylori) is a bacterial pathogen that infects half of the global population. VacA, a major H. pylori virulence factor, forms an anion-selective, hexameric channel in host cell membranes. No atomic detail of VacA's transmembrane (TM) channel has been revealed by experimental methods, while its structural prediction continues to challenge even state-of-the-art deep learning models. In this talk, I will discuss our recent study using a hierarchical workflow that employs atomistic MD simulations to model the VacA TM channel. Apart from validating our model by nine in silico mutations, we verify its selectivity for chloride over sodium ions and identify the role of VacA's non-transmembrane segment in stabilizing its TM channel. Combining simulations with liposome assays, we further demonstrate a previously uncharacterized feature of VacA, namely, its capacity to leak protons.
15:10 – 15:40 Kei-ichi Okazaki (Institute for Molecular Science)
Integration of AlphaFold with Molecular Dynamics for Efficient Simulation and Control of Biomolecular Machines
Biomolecular machines, such as motor and transporter proteins, change conformations when they function. First, I will introduce our approach, AF3-ReD, for predicting conformational changes by applying metadynamics-like biasing potentials to the diffusion generative model in AlphaFold3. Second, I will present AlphaFold-facilitated Markov state modeling, in which Markov state models are constructed from molecular dynamics data initiated from AlphaFold structures to analyze the transition pathway and free energy of a redox-driven transporter protein. Third, I will present our attempt to redesign the inhibitory peptide for FoF1 ATP synthase by constructing a machine learning model that incorporates the AI-predicted sequence variations and the peptide's predicted helical propensity.
15:40 – 16:10 Jejoong Yoo (KIAS)
Overcoming Biomolecular Modeling Challenges: AI Reconstruction and Accurate Simulation of IDPs and DNA-Binding Proteins
Simulating intrinsically disordered proteins (IDPs) and DNA-binding proteins is challenging because their highly flexible regions are systematically missing from experimental structures. To create complete, simulation-ready models, we introduce PATCHR, an AI-powered diffusion model that accurately inpaints these missing segments. However, even with complete structures, standard molecular dynamics (MD) force fields often overestimate non-bonded attractions, which artificially stalls physical movements during simulations. To resolve this, we present CUFIX-AMBER, an improved force field with carefully calibrated nonbonded and hydrogen bond parameters. Together, this toolkit bridges generative AI and physical simulation to successfully capture the true conformational ensembles of IDPs and the physical diffusion of proteins along DNA.
16:10 – 16:40 Kei Moritsugu (Osaka Metropolitan University)
Experimental Data-driven Structural Ensemble Construction of Linear Diubiquitin Using Multi-scale Simulation and Deep Generative Modeling
16:40 – 17:00 Coffee Break
17:00 – 18:00 Open Discussion

Day 2: May 29, 2026 (Fri)

08:30 – 09:00 Registration (2F, IIB)
Session 4: Multiscale Modeling & Coarse-Graining (Chair: Florence Tama)
09:00 – 09:30 Gregory Voth (University of Chicago)
Ongoing Advances in the Theory and Application of Coarse-graining
Advances in theoretical and computational methodology will be presented that are designed to simulate complex (biomolecular and other soft matter) systems across multiple length and time scales. This bottom-up approach provides a systematic connection between all-atom (AA) molecular dynamics, coarse-grained (CG) modeling, and mesoscopic phenomena. At the heart of these concepts are methods for deriving CG models from molecular structures and their underlying atomic-scale interactions. An important component of our work in the past few years has been the concept of the "ultra-coarse-grained" (UCG) model and its associated computational implementation. In the UCG approach, the CG sites or "beads" can have internal states, much like quantum mechanical states, so the UCG model involves a conceptual abstraction beyond simply Newtonian or Langevin dynamics for the CG beads. These internal states help to self-consistently quantify a more complicated set of possible interactions within and between the CG sites, while still maintaining a high degree of coarse-graining in the modeling. The presence of the CG site internal states also greatly expands the possible range of systems amenable to accurate CG modeling, including quite heterogeneous systems such as complex self-assembly processes such as occur for large multi-protein complexes. The development of bottom-up CG models from the underlying atomistic interactions also addresses special challenges in terms of the treatment of solvation, multi-body correlations, representability, transferability, and the missing entropy in CG models. Recent breakthroughs in addressing these issues – in particular by employing developments in machine learning – will be a focus of my talk. As time allows, one "pay-off" application from our multi-year effort will focus on processes from HIV-1 virus replication and especially on the maturation of the HIV-1 virus from several thousand proteins – a phenomenon involving a billion atoms or more over long timescales that cannot be approached through AA MD simulation.
09:30 – 10:00 Cecilia Clementi (Free University Berlin)
Modelling Protein Dynamics with Machine Learning and Molecular Simulation
The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of macromolecular processes and are limited in their temporal and spatial resolution. On the other hand, atomistic simulations are still not able to sample the conformation space of large complexes, thus leaving significant gaps in our ability to study molecular processes at a biologically relevant scale. We present our efforts to bridge these gaps, by exploiting the available data and using state-of-the-art machine-learning methods to design multiscale models for complex macromolecular systems. We show that it is possible to define simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements.
10:00 – 10:30 Shoji Takada (Kyoto University)
Protein-resolution modeling for cellular-scale simulations
10:30 – 10:40 Group Photo
10:40 – 11:00 Coffee Break
Session 5: Multiscale Methods, Generative AI & Simulation Infrastructure (Chair: Shoji Takada)
11:00 – 11:30 Yuji Sugita (RIKEN / University of Tokyo)
Multi-scale simulations of biomolecular condensates in cellular environments
11:30 – 12:00 Bernard R. Brooks (NIH)
An overview of recent applications of machine learning and AI tools for problems in biophysics at the NIH
This presentation consists of a survey of our recent efforts to use machine learning and AI tools for applications in biophysics. Some of the projects presented will include: Leveraging induced polarization for efficient IC50 prediction using minimal features. Predicting iron-sulfur cluster redox potentials using a model derived from protein Structures. Extending protein pKa prediction by tree-based machine learning using PKAD-R, a curated, redesigned and expanded database of experimental pKa values in proteins. Using AI to identify drug repurposing candidates from existing approved drugs that can be used in novel ways for treating unrelated specific cancers.
12:00 – 12:30 Abhishek Singharoy (Arizona State University)
TBA
12:30 – 13:00 Frank Noé (Microsoft Research)
TBA
13:00 – 14:30 Lunch
Session 6: From Quantum to ML (Chair: Yuko Okamoto)
14:30 – 15:00 Darrin York (Rutgers University)
Multiscale quantum and machine-learning models for drug discovery and enzyme design
This talk presents AI-driven multiscale modeling tools for drug discovery and enzyme design. We introduce end-state alchemical reservoirs and generalized restraint methods for absolute binding free energy calculations, and a Quantum Deep-learning Potential Interaction (QDπ) force field for drug-like molecules that can be applied for bespoke force field generation and indirect “book-ending” alchemical simulations. We further describe a computational enzymology framework that integrates enhanced sampling, free energy methods, and hybrid quantum/ML potentials to investigate enzymatic catalysis. A key development is the Flexible Inner Region Ensemble Separator (FIRES), a multi-component, multi-site approach for maintaining spatial separation between QM solute, QM solvent, and MM solvent regions. These tools enable detailed simulations of catalytic pathways and reveal design principles in natural and engineered nucleic acid enzymes.
15:00 – 15:30 Kwangho Nam (University of Texas at Arlington)
Accelerating Enzyme Catalysis Simulations with Multiscale and Machine Learning-Assisted QM/MM Methods
Understanding enzyme catalysis requires quantum mechanical/molecular mechanical (QM/MM) simulations that are both accurate and capable of accessing biologically relevant time and length scales. However, the high computational cost of conventional QM/MM simulations has long limited their routine application to complex enzymes. In this talk, I will present developments from our group that address these challenges through multiscale and machine learning-assisted QM/MM methodologies. Central to this effort is the multiple time step ai-QM/MM framework, which enable substantially larger time steps while preserving accurate free energy calculations. I will also discuss delta-machine-learning potential approaches that further accelerate QM/MM simulations. Applications to hydride transfer and phosphoryl transfer reactions will be presented, demonstrating how these approaches can efficiently and robustly simulate enzyme catalysis without sacrificing mechanistic detail.
15:30 – 16:00 Tohru Terada (University of Tokyo)
Multiscale Molecular Simulations for Elucidating Protein Function and Assembly Mechanisms
Proteins exhibit diverse functions, spanning from the catalysis of small biomolecule reactions, which necessitates the explicit consideration of electronic structures, to cellular scaffolding, which involves the large-scale assembly of numerous protein molecules. To fully elucidate these diverse protein function mechanisms, multiscale simulation approaches are indispensable. Our research employs hybrid quantum mechanical/molecular mechanical (QM/MM) methods to clarify the catalytic mechanisms of enzymes, and coarse-grained molecular dynamics (CGMD) simulations to investigate the assembly mechanisms of proteins involved in liquid-liquid phase separation (LLPS) and the spontaneous formation of cytoskeletons. In this workshop, I will present our recent findings utilizing QM/MM and CGMD simulations. I will also introduce our latest methodological developments, which include an artificial intelligence (AI)-accelerated transition state search tool and a rigid-body CGMD method specifically designed for large-scale protein assemblies.
16:00 – 16:30 Adrian Roitberg (University of Florida)
Machine Learning Potentials: much faster than quantum, somewhat slower than classical. Searching for the right spot.
16:30 – 17:00 Antonio Mirarchi (University Pompeu Fabra)
Transferable neural network potentials of protein thermodynamics
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.
17:00 – 17:30 Coffee Break / Open Discussion

Day 3: May 30, 2026 (Sat)

08:30 – 09:00 Registration (2F, IIB)
Session 7: Integrative & Data-Driven Modeling (Chair: Gregory Voth)
09:00 – 09:30 Gerhard Hummer (Max Planck Institute of Biophysics)
Learning from molecular simulations
09:30 – 10:00 Florence Tama (RIKEN / Nagoya University)
TBA
10:00 – 10:30 Alberto Perez (University of Florida)
Scaling Peptide Discovery with AI and Physics-Based Modeling
Peptide-mediated interactions play central roles in signaling, regulation, and disease, but their discovery and structural characterization remain challenging due to the flexibility and conformational heterogeneity of peptide-binding systems. Recent advances in AI-based structure prediction have expanded our ability to explore peptide–protein interactions at scale, enabling rapid interrogation of large peptide libraries and the identification of candidate binders that would be difficult to access experimentally alone. In this talk, I will discuss how we combine AI-driven structural prediction with physics-based modeling and experimental validation to develop scalable and predictive pipelines for peptide discovery. Using BET ET-domain interactions as a primary example, we employ AlphaFold-based competitive binding approaches to screen and prioritize peptide candidates across large sequence spaces. These AI methods provide a powerful framework for scaling discovery, but they also expose important limitations. In several systems, AlphaFold2 and AlphaFold3 predict distinct peptide orientations or even different binding regions for the same complex. While NMR chemical shift perturbation and exchange experiments confirm binding, they cannot distinguish which binding mode is correct. To resolve these ambiguities, we integrate physics-based ensemble modeling and enhanced conformational sampling through the MELD approach to recover the binding mode most compatible with the data and physics model. The resulting models enable the design of new miniprotein binders that experimentally bind the intended targets and engage the correct interfaces. Finally, I will discuss recent extensions of these approaches toward functional biomolecular engineering applications (miniprotein design to functionalize CAR-T cells) through our participation in the Bits to Binders Competition.
10:30 – 10:50 Coffee Break
Session 8: Complex Biomolecular Assemblies & Phase Behavior (Chair: Tohru Terada)
10:50 – 11:20 Joan-Emma Shea (UC Santa Barbara)
Self-Assembly of the Tau Protein: Liquid-Liquid Phase Separation and Fibrillization
Intrinsically disordered proteins (IDPs) belong to a class of proteins that do not fold to a well-defined, three-dimensional structure, but rather co-exist between interconverting, partially structured conformations. In the crowded cellular milieu, IDPs can form liquid droplets through liquid-liquid phase separation or self-assemble into beta-sheet enriched amyloid fibrils, processes linked to normal physiological functions and various pathologies. In this talk, we present a new computational framework that scores amyloid and LLPS propensities from protein language model (pLM) embeddings, thereby enabling rapid proteome-wide annotation of peptides and residues. We apply this framework to the Tau protein, an intrinsically disordered protein that plays an important role in stabilizing microtubules and identify segments of Tau that have a propensity to phase separate or form fibrils. Using a multiscale approach combining atomistic, coarse-grained, and field-theoretic simulations, we further characterize the mechanisms of fibrillization and phase separation of these fragments and shed light on the sequence characteristics linked with these two modes of assembly.
11:20 – 11:50 Xiangze Zeng (Hong Kong Baptist University)
Developing Temperature-Dependent Coarse-Grained Potentials for Simulating Phase Separation of Disordered Proteins
Thermoresponsive phase transitions of intrinsically disordered proteins (IDPs), including both upper critical solution temperature (UCST) and lower critical solution temperature (LCST) transitions, are widely observed in natural and synthetic sequences. However, most existing coarse-grained (CG) models with implicit solvents employ temperature-independent interactions and fail to capture solvation-driven LCST behavior. Here, we bridge the gap between atomistic hydrations and macroscopic phase separation. Leveraging extensive all-atom molecular dynamics simulations, we reveal a fundamental linear correlation between the temperature-dependent inter-residue interaction strengths and hydration free energies. Furthermore, we demonstrate that the heterotypic interactions at the molecular level can be well approximated by a simple combination of the homotypic interactions. We incorporate these thermodynamic insights into a physics-based framework, TEA (Temperature-dependent Energetics derived from hydrAtion), which introduces temperature-dependent potentials with minimal phenomenological fitting. The TEA-augmented CG models robustly distinguish UCST- and LCST-type sequences, successfully identify experimentally reported outliers, and accurately reproduce LCST-type single-chain compaction trends and phase diagrams of multiple disordered proteins. Our work provides a transferable and physically interpretable framework that bridges atomistic hydration thermodynamics and phase behavior of IDPs, enabling the simulation of thermoresponsive sequences with minimal phenomenological fitting.
11:50 – 12:20 Ana Damjanovic (Johns Hopkins University)
From Atoms to Neuronal Spikes: A Multiscale Simulation Framework
12:20 – 12:25 Closing Remarks — Gregory Voth (University of Chicago)

Poster Sessions

Setup: Day 1 morning (please put up your poster before lunch on May 28).

Removal: After Poster Session II on Day 2 (please take your poster down by the end of May 29).

Please leave your poster up for the full two days, regardless of your presentation day (Day 1 or Day 2).

Poster numbering: Day 1 presenters have odd numbers (P1, P3, …); Day 2 presenters have even numbers (P2, P4, …). Each board holds two posters (front and back) — one Day 1 and one Day 2 — so that only every other board is “active” during each session. Each board will be labeled with the poster number in advance; please mount your poster on the side labeled with your number.

Poster Session I — Day 1 (May 28, 2026, 18:00–20:00, 6F, IIB)
P1 Sandhya Tiwari (IPR, Osaka University)
Uncovering the role of dimerization on dynamics and inhibitor stability in human versus Xenopus LTA4H
P3 Hobeom Kim (Seoul National University)
GA-Score: Learning the Physics of Molecular Interactions for scoring
P5 Yichao Wu (Osaka University PRIMe)
Molecular Dynamics Simulation Study of the Pathogenic Mechanism Underlying the Ala711-Glu714 Deletion Mutation in the IGF1R/INSR Heterodimer
P7 Shweta Kumari (RIKEN)
Mutation-Dependent Changes in EGFR–Shc Recognition through Integrative Modeling, Molecular Dynamics Simulations, and Docking
P9 Minji Kim (Seoul National University)
GateMol-BBB: A gated Multimodal gMLP Framework for Blood-Brain Barrier permeability prediction
P11 Yangyang Zhang (Osaka University PRIMe)
Probing Oxytocin–OXTR Binding through Molecular Dynamics Simulations
P13 Myunghyun Jang (Seoul National University)
EquiFlex: Equivariant flow matching with physics-aware lossses for flexible protein-ligand complex struture prediction
P15 Jaeoh Shin (KIAS)
Evaluating BioEmu-Generated Kinase Ensembles Reveals Structure Selection as the Virtual Screening Bottleneck
P17 Ikhyeong Jun (Seoul National University)
Agentic AI-based automation of structure-based drug discovery workflow
P19 Sujith Sritharan (RIKEN R-CCS)
Cryo-EM and Conformational Heterogeneity of the Nucleosome
P21 Tingting Wang (RIKEN R-CCS)
Cryo-EM and MDSPACE Reveal Continuous Conformational Heterogeneity in Glutamate Dehydrogenase
P23 Tsuyoshi Kawai (Saitama University / RIKEN R-CCS)
AFM-Fold: Rapid Reconstruction of Protein Conformations from AFM Images
P25 Wenyang Zhao (RIKEN R-CCS)
Identifying the most plausible phase-retrieved electron density maps of biocomplexes using 3D CNN autoencoder resampling
P27 Ajeet Kumar Yadav (Nagoya University)
Resolving Differential Cryptochrome Dynamics Using Integrated Molecular Simulation Approaches
P29 Petlada Rattanasombat (Nagoya University)
SAXS-guided prediction of CraCRY structure via coarse-grained MD simulations
P31 Constantin Guyot (Institute for Molecular Science)
Redesign of the inhibitor peptide IF1 for F-type ATP synthase from active learning approach
P33 Martin Byzov (Nagoya University)
Computational Insights into Mosquito Mechanosensation: MD Simulations of NompC and Molecular Docking of Nan-Iav in Aedes aegypti
P35 Takehito Seki (Institute for Molecular Science)
Conformational Dynamics of Na⁺-Pumping NADH–Quinone Oxidoreductase during Na⁺ Translocation from AlphaFold-Facilitated Markov State Modeling
P37 Hideto Tsubouchi (The University of Tokyo)
TBA
P39 Kenta Shobu (Keio University)
Molecular insights into the coupling between retinal isomerization and protein dynamics in ChR2 mutants
Poster Session II — Day 2 (May 29, 2026, 17:30–18:30, 6F, IIB)
P2 Yosuke Teshirogi (The University of Tokyo)
CGRig: a rigid-body protein model with residue-level interaction sites for long-time and large-scale protein assembly simulation
P4 Tarun Maity (RIKEN PRI)
Microscopic Mechanics of Autonomous Self-Healing in AP-Ethylene Copolymers
P6 Diego Ugarte La Torre (The University of Tokyo)
CGBack: Recovering atomistic detail from coarse-grained protein structures using diffusion models
P8 Jaemin Yoo (Sungkyunkwan University)
Balancing Protein–Nucleic Acid Interactions in ff19SB-OPC through Pair-Specific Lennard-Jones Corrections
P10 Mohamed Marzouk Sobaih (Ain Shams University)
Molecular Dynamics and Free-Energy Analyses Reveal Glycan-Driven Destabilization of the IGF Ternary Complex by the D440N ALS Mutation
P12 Jun Ohnuki (Institute for Molecular Science)
Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model
P14 Sosuke Asano (Keio University)
An Unsupervised deep learning for identifying characteristic amino acid residues from molecular dynamics simulations of similar systems
P16 Dongwoo Kim (Seoul National University)
A2Holo: Dual-Stream Flow Matching of 3D Structure and Protein Language Model for Conformational Transition Prediction
P18 Cheng Tan (RIKEN R-CCS)
Dual-Scale Mechanism of Condensate Regulation: Nonspecific Electrostatics Paired with Specific Helical Recognition in Hero11-TDP43
P20 Jinyoung Byun (Seoul National University)
Steering Diffusion-based Co-folding model with Physics: Feynman–Kac Sampling under Amber Force-Field Constraints
P22 Elisa Rioual (RIKEN PRI)
When In-Cell NMR Meets MD: Insights into GB1 in Crowded Environments
P24 Yoobeen Shin (Seoul National University)
Quality-Aware Structural Tokenization for Protein Backbone Representation and Generation
P26 Haelyn Kim (Seoul National University)
FoldMapper: Inverse Folding via Structure-Derived Evolutionary Profiles and Cascaded Graph Encoding
P28 Ben Cree (RIKEN R-CCS)
Automated de novo Design of Nanographene inhibitors for Cryptochromes
P30 Jinung Song (Seoul National University)
Design of self-assembling protein nanoparticle using computational methods
P32 Tomoshi Kameda (AIST)
Predicting the evolution of the covid-19 using steered MD simulation and machine learning
P34 Csongor Németh (Vrije Universiteit Amsterdam)
De Novo Protein Design of Chemokine Binders Targeting CXCL12 and CCL25
P36 Shintaroh Kubo (RIKEN)
Computational investigation of how post translational modifications alter microtubule structure and charge to modulate dynein-2
P38 Naonobu Kuribayashi (Keio University)
TBA

Exploring Kobe

For participants interested in exploring Kobe during their stay, the following sites provide useful local information:

Organizing Committee

Gregory Voth (University of Chicago)
Yuji Sugita (RIKEN / University of Tokyo)
Florence Tama (RIKEN / Nagoya University)
Kei Moritsugu (Osaka Metropolitan University)
Yasuhiro Matsunaga (Saitama University / RIKEN)

Co-hosted & Sponsors

Contact

For inquiries, please contact:

Aya Takeuchi - aya.takeuchi at riken.jp

Updates