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Comparison of Linear and Nonlinear Classification Algorithms for the Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms.

, , , , , and . J. Chem. Inf. Comput. Sci., 43 (6): 2019-2024 (2003)

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EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python., , , , , , and . LOD (2), volume 13811 of Lecture Notes in Computer Science, page 249-264. Springer, (2022)Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations., , , , , , , , , and 2 other author(s). J. Chem. Inf. Model., 61 (9): 4521-4536 (2021)Mechanistic Interpretation of Machine Learning Inference: A Fuzzy Feature Importance Fusion Approach., , , , , and . CoRR, (2021)Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models., , and . J. Chem. Inf. Model., 55 (7): 1316-1322 (2015)Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery., , , , , and . CoRR, (2019)European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials., , , , , , , , , and 4 other author(s). J. Cheminformatics, 14 (1): 57 (2022)Materials for stem cell factories of the future, , , , , , , , , and . Nature Materials, 13 (6): 570--579 (May 2014)Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds., , and . J. Chem. Inf. Model., 53 (1): 223-229 (2013)Toward Interpretable Machine Learning Models for Materials Discovery., , and . Adv. Intell. Syst., 1 (8): 1900045 (2019)Feature importance in machine learning models: A fuzzy information fusion approach., , , , , , , and . Neurocomputing, (2022)