Posters
Posters
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Poster Session 1
| Poster Number | Name | Affiliation | Title |
|---|---|---|---|
| 1 | Albert Ortega BartolomĂ© | Institut de QuĂmica avançada de Catalunya (CSIC) | NEMAT: An Automated Non-Equilibrium Free-Energy Framework for Predicting Ligand Affinity in Membrane Proteins |
| 2 | Alexandre Blanco-González | MRC - Laboratory of Molecular Biology | Training a force field for proteins and small molecules from scratch |
| 3 | Alyssa Travitz | Open Free Energy, OMSF | Open Free Energy: An Ecosystem for Open Source Alchemy |
| 4 | Anna Katharina Picha | University of Vienna | Dual-coordinate Alchemical Transformations Using Machine-Learned Interatomic Potentials |
| 5 | Audrius Kalpokas | University of Edinburgh | Modelling Macrocyclic Peptide PCSK9 Inhibitors with Alchemical Free Energy Calculations |
| 6 | Benjamin Kaminow | Memorial Sloan Kettering Cancer Center | Exploring the relevance of structure-based machine learning in binding affinity predictions |
| 7 | Blanca DĂaz Canals | Universitat de Barcelona | Molecular glues in drug design: enhancing protein-protein interaction stability for innovative cancer therapies |
| 8 | Charlie Holdship | University of Southampton | Computational Alanine Scanning of Conotoxin Peptides as a Route to Antitoxin Design |
| 9 | Chenggong Hui | Max Planck Institute for Multidisciplinary Sciences | Enhancing Relative Binding Free Energy Calculation with Grand Canonical Monte Carlo, Water Swap Monte Carlo, and Replica Exchange Solute Tempering |
| 10 | David De Sancho | University of the Basque Country | Decoding the Rules of Phase Separation through alchemical transformations on model peptides |
| 11 | David Dotson | Datryllic LLC | Efficient and fully automated large scale execution of alchemical campaigns with alchemiscale |
| 12 | David Sjöberg | Uppsala University | An Explainable and Uncertainty-Aware Bayesian Graph Neural Network for Predicting the effect of Protein mutations |
| 13 | Dominic Rufa | SandboxAQ | Nonequilibrium Chimeric Switching (NEX) for parallelizable Relative Binding Free Energy Calculations |
| 14 | Emily Landwehr | Indiana University School of Medicine | Exploration of alternative λ-networks in LaDyBUGS (λ-Dynamics with Bias Updated Gibbs Sampling) to optimize sampling efficiency |
| 15 | Enrico Ruijsenaars | ETH ZĂĽrich | Scalable Multistate Free Energy Calculations with automated Network Design |
| 16 | Fazil Safarov | Zuse Institute Berlin | Targeting Undruggable Proteins Using ISOKANN framework |
| 17 | Finlay Clark | Newcastle University | Fast training of Open Force Field valence parameters for free energy calculations |
| 18 | Gaetano Calabro | OpenEye Cadence Molecular Sciences | Fast Free Energy Prediction with FE-NES |
| 19 | Haolin Du | University of Edinburgh | Scaling Absolute Binding Free Energy Calculations for Virtual Screening |
| 20 | Hiroyuki Ogawa | Shionogi & Co., Ltd | In silico-driven protocol for hit-to-lead optimization: a case study on PDE9A inhibitors |
| 21 | Hyesu Jang | OpenEye, Cadence Molecular Sciences | Rational iterative structure-based drug design in Orion |
| 22 | Irfan Alibay | Open Free Energy, The Open Molecular Software Foundation | Exploring the sensitivity of relative binding free energy calculations to force field choices |
| 23 | Hannah Baumann | Open Free Energy (OMSF) | Exploring the sensitivity of alchemical methods to force field |
| 24 | Justina Ratkeviciute | University of Southampton | Improving Alchemical Binding Free Energy Calculations Using Fully Adaptive Simulated Tempering (FAST) |
| 25 | Lucas Mateos | Computational Biochemistry Unit (CINN/CSIC) | Structural characterization of cannabidiol and cannabigerol orthosteric and allosteric binding to the adenosine A3 receptor |
| 26 | Marc Schuh | BOKU Vienna | Scaling of non-bonded inter-molecular interactions improves convergence in the geometric route for protein-protein binding free energy calculations |
| 27 | Marco Klähn | AstraZeneca | Generative Active Learning for Molecular Design: Integrating Compound Synthesizability Prediction with Binding Affinity Optimization |
| 28 | Maria Cecilia Barrera | IMEC | Relative Binding Free Energy pipeline for G Protein Coupled Receptors |
| 29 | Mert Sagiroglugil | University of Barcelona | From Unbiased MD to Selectivity: Neural-Network Metastable State Discovery and MSM Kinetics for Bioorthogonal Analogs |
| 30 | Monica Barron | Indiana University School of Medicine | Comparing co-alchemical ion and analytic correction charge-changing perturbation strategies for modeling protein mutations in λ-dynamics |
| 31 | Nadine Grundschober | BOKU University | Site-directed mutagenesis applying A-EDS |
| 32 | Niu Huang | National Institute of Biological Sciences, Beijing | - |
| 33 | Oscar Diaz Sanzo | Nanomaterials and Nanotechnology Research Center (CINN-CSIC) | Rational design of novel pyrimidinone derivatives as dual A2A/A2B adenosine receptor antagonists |
| 34 | Parveen Gartan | Department of Chemistry, University of Bergen | Multisite λ Dynamics in Academic Drug Design Projects |
| 35 | Lindsey Whitmore | University of Colorado Boulder | Scalable, accurate, and adaptive free energy calculations for molecular design |
| 36 | Aleix Quintana | Universitat Autònoma de Barcelona | Non-equilibrium alchemical thermodynamic integration to calculate accurate free energy differences in biomolecular systems |
Poster Session 2
| Poster Number | Name | Affiliation | Title |
|---|---|---|---|
| 37 | Donald van Pinxteren | Groningen University | Stabilizing Large Alchemical Perturbations in Relative Binding Free Energy Calculations |
| 38 | Shen Guo | Groningen University | QGPU: A GPU-Accelerated Molecular Dynamics Engine |
| 39 | David Alencar Araripe | Groningen University | Fast GPCR Ligand RBFE in All-Atom Lipid Bilayers |
| 40 | Remco L van den Broek | Leiden University | TACTICS: Bayesian Active Learning for Compound Prioritization in Ultra-Large Combinatorial Libraries Toward Free Energy Calculations |
| 41 | Mark Fonteyne | Leiden University | Validating peptide-probe unbinding with contact parallel cascade selection molecular dynamics (cPaCS-MD) for fluorescent-guided surgery |
| 42 | Wessel Porschen | Leiden University | An integrated residue and ligand free energy perturbation protocol for membrane proteins |
| 43 | Cheil Jespers | Leiden University | QmapFEP - A flexible infrastructure for high-throughput FEP calculations with spheric boundary conditions |
| 44 | Qinghua Liao | University of Barcelona | Applications for Alchemical Free Energy Calculations of Solvation, Binding and pKa in Complex Biomolecular Systems |
| 45 | Sara Tkaczyk | University of Vienna | Alchemical free energy calculations with neural network potentials |
| 46 | Sarah Stieglitz | Indiana University School of Medicine | Optimizing and Applying LaDyBUGS for Accurate Modeling of Protein-Peptide Binding Free Energies |
| 47 | Shu-Yu Chen | University of Basel | Smoother Alchemical Transformation via Enveloping Distribution Sampling for Free-Energy Estimation |
| 48 | Simon Webb | VeraChem LLC | Fast, accurate prediction of protein-ligand binding free energies by mining minima: the VM2 software package |
| 49 | Yutong Zhao | NVIDIA | Accelerating Molecular Dynamics |
| 50 | Zhao Chen | National Institute of Biological Sciences, Beijing | Exploring the Energetic Contributions of Halogen Atoms to Protein-Ligand Interactions |
| 51 | Pavel Buslaev | Astex Pharmaceuticals | Free Energy Calculations in Fragment-Based Drug Discovery: Learnings from Internal Validation Studies |
| 52 | Varbina Ivanova | University of Barcelona | TOWARDS ACCURATE BINDING MODE PREDICTION FOR FREE-ENERGY CALCULATIONS: DEVELOPMENT OF THE MULTIPLE-COPIES ASSOCIATION STUDIES (MAS) |
| 53 | Nithishwer Mouroug Anand | University of Southampton | Optimizing ABFE Workflows for applications to membrane proteins GPCRs |
| 54 | Aitor Valdivia | Universitat de Barcelona (UB) | Mining Druggable Sites in Influenza A Hemagglutinin: Binding of the Pinanamine-Based Inhibitor M090 |
| 55 | Anna M. Herz | Boehringer Ingelheim | Optimising Potency Predictions: When and How FEP Data Improves Machine Learning Models |
| 56 | Katerina Barmpidi | University of Barcelona | Decoding Isoform Selectivity in AMPK via Free Energy Calculations |
| 57 | Leon Persch | Johannes Gutenberg-University Mainz | Folding Free Energy Perturbation Reveals a Critical Role of the PGLE Motif in Vreteno ZNF Stability |
| 58 | Ivan Manoza | QMUL | Computational assay of hERG ion permeation, small-molecule modulation, and binding free energies |
| 59 | Ravy Leon Foun Lin | AUDENSIEL & Université Paris Cité | N-Glycosylation Dynamics and Conformational Free Energy Landscapes in Full-Length IgG2 & IgG4 Monoclonal Antibodies: An All-Atom Molecular Dynamics Investigation |
| 60 | Lorenzo Tulli | University of Bristol | Cancer-associated mutations reconfigure dynamical responses in EGFR kinase |
| 61 | Jasmin GĂĽven | University of Bristol | Towards improved binding affinity calculations for metalloproteins with meze |
| 62 | Yuanqing Wang | New York University / University of Toronto | Estimator, the sampler, and the force field: These three are one |
| 63 | Matthew Burman | The Institute of Cancer Research | FEP protocols for real world potency optimisation |
| 64 | Aakash Davasam | Weill Cornell / Rockefeller University / Memorial Sloan Kettering Cancer Center | Does the membrane matter? Assessing the explicit membrane protocol in OpenFE |
| 65 | Icaro A. Simon | University of Copenhagen | Fast Screens or Rigorous Ranking? AI Cofolding Models versus Free Energy Perturbation for GLP-1 Variant Binding to GLP1R |
| 66 | Carter Wilson | Max Planck Institute for Multidisciplinary Sciences | Computational alchemy for studying protein covalent modifications |
| 67 | Alex Payne | Memorial Sloan Kettering Cancer Center | How many crystal structures do you need to trust your docking results? |
| 68 | BinQing Wei | Genentech | An AI Agent for Streamlining Binding Free Energy Calculations |
| 69 | Ernestas Urniežius | Vilnius University | From Binding Thermodynamics to Free Energy Prediction: Molecular Recognition in Carbonic Anhydrase–Sulfonamide Model Systems |
| 70 | Iván Pulido | Memorial Sloan Kettering Cancer Center | Nonequilibrium Alchemical Free Energy Calculations for Protein Mutations Using OpenFE: Application to ABL1 Binding Systems |
| 71 | Sukrit Singh | Memorial Sloan Kettering Cancer Center | More protein-ligand data are needed for AlphaFold-like models to enable drug discovery |
| 72 | Tatjana Braun | Schrodinger | Accurate in silico prediction of pH-dependent antibody binding affinities using robust physics-based methods |