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
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