During the conference there will be two poster sessions. A RED session on Monday (15.30 – 17.30) and a BLUE session on Tuesday (15.30 – 17.30) in the exposition. The poster titles and pdfs can be shared here after the conference if the authors choose to.
Session RED
Advanced Cheminformatics Techniques
| P01 | Using ChEMBL to improve molecule generation Eloy Felix Manzanares  , EMBL-EBI |
| P03 | Chemical probes in the scientific literature: Illuminating novel target-disease associations and strengthening the existing evidence Melissa Adasme , EMBL-EBI |
| P05 | Federated representation learning using knowledge distillation Thierry Hanser , Lhasa Limited |
| P07 | Taming Tautomers: Extending ECFP for Tautomer Invariance  Rachael Mary Elizabeth Pirie , NextMove Software |
| P09 | Practical Molecular Property Prediction with MolPipeline – A Scientific Industry Perspective Conrad Stork, BASF SE |
| P11 | Integrating in Silico Enrichment Prediction with de Novo Building Block Selection for Efficient Combinatorial Library Design Remco Leendert Van Den Broek , Leiden University |
| P13 | MolMeDB – Molecules on Membranes Database Kateřina Storchmannová , Palacky University in Olomouc |
| P15 | The next generation of the IUPAC International Chemical Identifier (InChI) Gerd Blanke , StructurePendium Technologies GmbH |
| P17 | ProQSAR: An End-to-End Framework for the Automated Construction of Predictive Models Tieu Long Phan, University Leipzig |
| P19 | Prefix-based decision tree for faster generation of SMARTS-based fingerprints Andrew Peter Dalke, Dalke Scientific |
| P21 | A Comprehensive Mapping of Chemical and Pharmacological Spaces for Drug Discovery and Repurposing: Insights from Marketed Drugs and Drug Candidates Candida Manelfi, Dompé Farmaceutici S.p.A. |
| P23 | Tangible Chemical Space: Easy Access to a Fully Annotated Database of Compounds by Enumeration of Two- or Three-Component Reactions Valerio Tazzari, Dompé Farmaceutici S.p.A. |
Artificial Intelligence, Machine Learning, and QSAR
| P25 | The Compound Mapper: bridging practitioners and bioactivity data with automated quality control David Alencar Araripe , Leiden University |
| P27 | Discovering novel beta-lactamase inhibitors with an AI-based virtual pipeline Helle van den Maagdenberg , Leiden University |
| P29 | Balancing Complexity and Efficiency: Scalable Machine Learning Approaches for Reaction Yield Prediction Idil Ismail , ETH Zurich |
| P31 | Bio-Isostere Guided Molecular Property Prediction Anatol Ehrlich, University of Vienna |
| P33 | Prediction of in vivo PK Profiles from Chemical structures and in vitro ADME Experiments Moritz Walter , Boehringer Ingelheim Pharma GmbH & Co. KG |
| P35 | Censored Loss: Inclusion of Censored Data in Molecular Affinity Modelling Marc Boef, Leiden University |
| P37 | Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data Hosein Fooladi , University of Vienna |
| P39 | Pragmatic in silico approach to reducing the risk of unexpected toxicity events in late-stage drug development Huanni Zhang , University of Vienna |
| P41 | Fast and Scalable 3D Pharmacophore Screening with PharmacoMatch Daniel Rose , University of Vienna |
| P43 | Unlocking the Potential of C2-Carboxylated 1,3-Azoles: A Computational Design-Make-Test-Analyze (DMTA) Approach Kerrin Janssen, Technische Universitaet Braunschweig |
| P45 | Application of DFT to assess nitrosamine formation risks: Tertiary amines aren’t a risk, except when they are Emma Louise Pye, Lhasa Limited |
| P47 | Closing the generative AI SBDD loop: From GPCR structure to reinforcement learning guided de novo ligand design and back again Chris de Graaf , Structure Therapeutics |
| P49 | Conformal calibration of QSAR classifiers Sebastien Guesne, Lhasa Limited |
| P51 | ANNalog – Generation of MedChem-similar molecules Wei Dai, Queen Mary University of London |
| P53 | Improving affinity of EpCAM-binding peptides using AlphaFold2 for in vivo tumor imaging Nada Badr , Leiden University Medical Centre |
| P55 | Integration of stereochemistry within DrugEx for better sample efficiency Chiel Jespers , Leiden University |
| P57 | Multi-task is what you need! Multi-task machine learning models for molecular property prediction Bart Lenselink , Galapagos |
| P59 | Fantastic SMILES augmentation methods and where to find them Helena Brinkmann , Eindhoven University of Technology |
| P61 | Constrained generation of molecules using Diffusion Models Cristian-Catalin Pop, University of Medicine and Pharmacy Iuliu Hatieganu |
| P63 | A Novel Statistical Machine Learning Framework for Enhanced Drug Safety Prediction in Zebrafish Assays Filippo Lunghini, Dompé Farmaceutici S.p.A. |
| P65 | Predicting the Dissipation Kinetics of Agrochemicals in Soil Vincent-Alexander Jean-Luc Christopher Mortimer Scholz, University of Vienna |
| P67 | NLP-inspired Operators for De novo Design Augmentation Hanz Tantiangco, Information School, The University of Sheffield, U.K. |
Integrative Structure-Based Drug Design
| P69 | Extending Enveloping Distribution Sampling Towards NE-EDS: A Non-Equilibrium Approach to Free-Energy Estimation Enrico Martin Ruijsenaars, ETH Zurich |
| P71 | IMERGE-FEP 2.0: Generating intermediate R-groups for challenging free energy perturbations Daan Antoon Jiskoot , Rijksinstituut voor Volksgezondheid en Milieu |
| P73 | Assessing the role of machine learning-based pose sampling in virtual screening Thi Ngoc Lan Vu , University of Vienna |
| P75 | Ultralarge Tailored Library of Amino Acid Derivatives Designed to Target Peptide GPCRs Szymon Pach , Uppsala Universitet |
| P77 | Oxytocin-Signaling-Inspired Allosteric Modulator Design for the µ-Opioid Receptor Marvin Taterra, University of Münster |
| P79 | Improved Protein-Ligand Structure Modeling using Conserved Scaffold Placement Jonathan Pletzer-Zelgert , University of Hamburg |
| P81 | Structure-based Pharmacophore Screening of TREM2 Kevin Lam , Freie Universität Berlin |
| P83 | Identification, Synthesis and Biological Assessment of Allosteric Ligands for the C-C Chemokine Receptor 5 Kian Noorman van der Dussen, Rijksuniversiteit Groningen |
New Modalities and Large Chemical Data Sets
| P85 | Generation of Custom Synthetically Accessible Combinatorial Chemical Spaces using Machine Learning-Based Reagent Filtering – Design of Freedom Space 4.0 Speaker, Affiliation |
Open Science, Omics, and Natural Products
| P87 | Identifying off-target drug interactions mediated via DNA methylation Delaney Amati Smith, Stanford University |
| P89 | VHP4Safety Compound Wiki: an open science approach to collect domain specific knowledge Egon Willighagen   , Maastricht University |
| P91 | A mutator effect caused by two amino acid changes in the DNA binding region of M. smegmatis DnaE1: Novel insights into DNA polymerase fidelity using in silico and in vivo approaches Rosan Kuin , Leiden University |
Session BLUE
Advanced Cheminformatics Techniques
| P02 | STELLAR: Developing and optimizing a novel advanced docking protocol for processing large peptides without AI assistance. A cancer context application Alejandro Rodríguez-Martínez, Universidad Católica de Murcia UCAM |
| P04 | Towards More Reliable Distance Geometry-Based Conformer Generation Niels Maeder, ETH Zürich |
| P06 | Investigating Structural Information in Graph-based Neural Fingerprints for Similarity Searches Samuel Kurt Robert Homberg , University of Münster |
| P08 | Automatic Annotation of Sites of Metabolism from Substrate-Metabolite Pairs Roxane Axel Jacob , University of Vienna |
| P10 | Bayesian Illumination: Inference and Quality-Diversity Accelerate Generative Molecular Models Jonas Verhellen, University of Copenhagen |
| P12 | The Molecule Fragmentation Framework (MORTAR): a rich client application for algorithmic substructure extraction Jonas Schaub , Friedrich Schiller University Jena |
| P14 | Retrospective evaluation of molecule enumeration methods Pierre-Yves Libouban , Johnson & Johnson |
| P16 | Deconvolution of “Origin-of-life” Reaction Networks Nico Domschke, Leipzig University |
| P18 | Deciphering Molecular Embeddings with Centered Kernel Alignment Matthias Welsch, University of Vienna |
| P20 | Predicting off-targets from ChEMBL data using the polypharmacology browser Maedeh Darsaraee, University of Bern |
| P22 | COMPASS: COMputational Pocket Analysis and Scoring System Akash Deep Biswas, Dompé Farmaceutici S.p.A. |
| P24 | LACAN: Leveraging adjacent co-occurrence of atomic neighborhoods Wim F. Dehaen, UCT Prague |
Artificial Intelligence, Machine Learning, and QSAR
| P26 | Enriching ChEMBL assay descriptions using Natural Language Processing Melissa Adasme , EMBL-EBI |
| P28 | Multiretro – A Synthesis Planning Toolkit Alan Kai Hassen , Pfizer Inc. |
| P30 | Robust Prediction of the Pharmacophore Fit Scores with Active Learning Daria Goldmann, Sanofi-Aventis Deutschland GmbH |
| P32 | Using deep learning and machine learning-based docking to investigate metalloenzyme-substrate complexes Daniil Lepikhov, Technische Universitat Berlin |
| P34 | From Theory to Practice: Reaction Similarity Search in Real-World Applications at Astex Ivan Derbenev, Astex Pharmaceuticals |
| P36 | Prediction of Pharmacokinetics Profile as Time Series Uday Abu Shehab , University of Vienna |
| P38 | Chemistry-aware foundation model for Small Molecule ADMET and Polypharmacology Property Estimation Pietro Morerio , Istituto Italiano di Tecnologia |
| P40 | Exploiting SARkush and Free-Wilson Analysis to Accelerate an Antiviral Drug Discovery Project Jess Stacey, MedChemica Ltd |
| P42 | Efficient compound selection strategies in lead optimization: insights from retrospective analysis Marc Bianciotto , Sanofi R&D |
| P44 | regAL: Python Package for Active Learning of Regression Problems Elizaveta Surzhikova, TU Braunschweig |
| P46 | Exploration of Data from the Pharmaceutical Industry for Site-of-Metabolism Prediction Ya Chen , University of Vienna |
| P48 | PySSA: end-user protein structure prediction and visual analysis with ColabFold and PyMOL Achim Zielesny , Westphalian University of Applied Sciences |
| P50 | REST2-AMP/MM: Integrating Enhanced Sampling with Machine Learning Potentials for Molecular Conformational Sampling Riccardo Solazzo, ETH Zurich |
| P52 | Cooperative Free energy: Induced PPI, Solvation, and Conformation in Protein-Ligand-Protein Ternary Complexation Shu-Yu Chen , ETH Zurich |
| P54 | A Feature-Engineered Delta-ML Approach for Molecular Structure Refinement: Bridging Exploration and Exploitation in Computational Chemistry Federico Lazzari , Scuola Normale Superiore |
| P56 | Machine Learning Predictions of the Protein-Ligand Binding Affinity with Fingerprints, Shape and Electrostatics Katarina Stanciakova, OpenEye, Cadence Molecular Sciences |
| P58 | Balancing Data Quantity and Quality: Evaluating Curation Strategies for Bioactivity Prediction Models Carl Schiebroek, ETH Zurich |
| P60 | PROTAC-Splitter: An AI-Based System to Automatically Identify PROTAC Ligands Stefano Ribes , Chalmers University of Technology and University of Gothenburg |
| P62 | The predicTeam’s All-Inclusive Strategy for Leveraging the Full Potential of ADMET Predictions in Drug Discovery Lara Kuhnke, Bayer AG |
| P64 | Molecular deep learning at the edge of chemical space Derek van Tilborg, Eindhoven University of Technology |
| P66 | Integrating Structural and Morphological Fingerprints: Understanding Information for Pattern Identification and Better Toxicity Prediction Floriane Stephanie Christelle Odje, Universität des Saarlandes |
| P68 | Gut Microbiota Metabolic Mimicking Drugs for Autoimmune/Infectious Diseases Shayma El-Atawneh , University of Münster |
Integrative Structure-Based Drug Design
| P70 | Molecular Dynamics and Experimental Insights into the Fungistatic Mechanism of Mutanobactin D Patricia Brandl, ETH Zurich |
| P72 | Benchmarking state-of-the-art In silico peptide design methods and evaluating peptide-optimization for fluorescent probes Mark Fonteyne , Leiden University Medical Center |
| P74 | Benchmarking AI-Driven Protocols for Cyclic Peptide Conformer Prediction and Ranking Rodrigo Ochoa , Novo Nordisk A/S |
| P76 | Active learning FEP using 3D-QSAR for prioritizing bioisosteres in medicinal chemistry Venkata Ramaswamy, Cresset |
| P78 | PIEZO1: In-Silico Investigation of Channel Activation and Deactivation Joana Massa, University of Münster |
| P80 | Pharmacophore-based Discovery of Novel Cytochrome P450 (CYP) 4A11 Inhibitors to Combat Non-alcoholic Fatty Liver Disease (NAFLD) Clemens Alexander Wolf, Freie Universität Berlin |
| P82 | Database AutoPH4: Pharmacophore Analysis of Multiple Protein Structures Steve Maginn, Chemical Computing Group |
| P84 | From 45 billion small molecules to potential new ligands for the intracellular binding site of CCR2, using High Throughput Virtual Screening, Molecular docking and Relative Free Energy Perturbation Donald Jonannes Marius van Pinxteren, Rijksuniversiteit Groningen |
New Modalities and Large Chemical Data Sets
| P86 | Scaffold-Based Library Design vs. Make-on-Demand Space: A Comparative Assessment of Chemical Content Leonard Bui, University of Strasbourg |
Open Science, Omics, and Natural Products
| P88 | Biosynfoni: A Biosynformatic Molecular Descriptor for Natural Product Research Lucina-May Nollen , Leiden University |
| P90 | Drug and clinical candidate drug data in ChEMBL Fiona Hunter , EMBL-EBI |