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Industry-Scale Orchestrated Federated Learning for Drug Discovery., , , , , , , , , и 37 other автор(ы). AAAI, стр. 15576-15584. AAAI Press, (2023)Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models., , , , и . CoRR, (2024)MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information., , , , , , , , , и 39 other автор(ы). J. Chem. Inf. Model., 64 (7): 2331-2344 (2024)Temporal Evaluation of Probability Calibration with Experimental Errors., , , , и . AIDD@ICANN, том 14894 из Lecture Notes in Computer Science, стр. 13-20. Springer, (2024)Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels., , , , , и . CoRR, (2024)Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein-Ligand Predictions., , , и . J. Chem. Inf. Model., 60 (10): 4546-4559 (2020)Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse., , , , , и . AIDD@ICANN, том 14894 из Lecture Notes in Computer Science, стр. 98-115. Springer, (2024)Orthologue chemical space and its influence on target prediction., , , , , , , , и . Bioinform., 34 (1): 72-79 (2018)QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design., , , и . J. Chem. Inf. Model., 64 (14): 5365-5374 (2024)Temporal Evaluation of Uncertainty Quantification Under Distribution Shift., , , , и . AIDD@ICANN, том 14894 из Lecture Notes in Computer Science, стр. 132-148. Springer, (2024)