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A Probabilistic Approach to Classifying Metabolic Stability
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Elements of Statistical Learning
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The Nature of Statistical Learning Theory
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Neural Networks: Tricks of the Trade
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ADME evaluation in drug discovery
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Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
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Drug discovery and drug metabolism
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Can we estimate the accuracy of ADME–Tox predictions?
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(Q) SAR study on the metabolic stability of steroidal androgens
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Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
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ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors
inferredcites →https://doi.org/10.1021/ci034233wopenalex-snapshot
Feature Selection for Descriptor Based Classification Models. 2. Human Intestinal Absorption (HIA)
inferredcites →https://doi.org/10.1021/ci040020aopenalex-snapshot
ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors [J. Chem. Inf. Comput. Sci. 43, 2137−2152 (2003)]
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Classifying ‘Drug-likeness' with Kernel-Based Learning Methods
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Ensemble Methods for Classification in Cheminformatics
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A Comparative Study on Feature Selection Methods for Drug Discovery
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Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
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Development and Validation of k-Nearest-Neighbor QSPR Models of Metabolic Stability of Drug Candidates
inferredcites →https://doi.org/10.1021/mp0700413openalex-snapshot
Machine Learning Models for Lipophilicity and Their Domain of Applicability
inferredcites →https://doi.org/10.1023/b:jcam.0000021861.31978.daopenalex-snapshot
Prediction of in vitro metabolic stability of calcitriol analogs by QSAR
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Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods
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Regression and Classification Using Gaussian Process Priors
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Neural Networks for Pattern Recognition
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An introduction to kernel-based learning algorithms
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Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships
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Metabolic Stability for Drug Discovery and Development
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Machine Learning Techniques for In Silico Modeling of Drug Metabolism
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In Silico Metabolism Studies in Drug Discovery: Prediction of Metabolic Stability
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Assessing Approximate Inference for Binary Gaussian Process Classification
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Advances in Large-Margin Classifiers
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Gaussian Processes for Machine Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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DEVELOPMENT AND APPLICATIONS OF GLOBAL ADMET MODELS
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In Silico Approaches to Address Compound Attrition
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From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ
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Visual Interpretation of Kernel‐Based Prediction Models
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Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance
inferred← citeshttps://doi.org/10.1002/minf.70018openalex-snapshot
MetaStab‐Analyzer: Classification and Regression Models for Metabolic Stability Prediction
inferred← citeshttps://doi.org/10.1002/wcms.1479openalex-snapshot
Advances in the prediction of mouse liver microsomal studies: From machine learning to deep learning
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Some Trends in Chem(o)informatics
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Prediction of sites of metabolism in a substrate molecule, instanced by carbamazepine oxidation by CYP3A4
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Predicting ADME properties in drug discovery
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In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods
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Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World
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Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence
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Pharmaceutical Perspectives of Nonlinear QSAR Strategies
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Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set
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Bias-Correction of Regression Models: A Case Study on hERG Inhibition
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A Maximum Common Subgraph Kernel Method for Predicting the Chromosome Aberration Test
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Strategic Use of Plasma and Microsome Binding To Exploit in Vitro Clearance in Early Drug Discovery
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Determination of Water Content in Automobile Lubricant Using Near-Infrared Spectroscopy Improved by Machine Learning Analysis
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Prediction of Metabolic Stability of Xenobiotics by the Pass and Gusar Programs
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Fast rule-based bioactivity prediction using associative classification mining
inferred← citeshttps://doi.org/10.1186/s13321-021-00542-yopenalex-snapshot
How can SHAP values help to shape metabolic stability of chemical compounds?
inferred← citeshttps://doi.org/10.1371/journal.pone.0008057openalex-snapshot
A Large Scale Analysis of Information-Theoretic Network Complexity Measures Using Chemical Structures
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New Polynomial-Based Molecular Descriptors with Low Degeneracy
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inferred← citeshttps://doi.org/10.1517/17425250902753261openalex-snapshot
The use of machine learning and nonlinear statistical tools for ADME prediction
inferred← citeshttps://doi.org/10.1517/17425255.2011.537257openalex-snapshot
Quantitative structure–pharmacokinetic relationships
inferred← citeshttps://doi.org/10.18097/pbmc20216703295openalex-snapshot
Predict of metabolic stability of xenobiotics by the PASS and GUSAR programs
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Two-Dimensional (2D) In Silico Models for Absorption, Distribution, Metabolism, Excretion and Toxicity (ADME/T) in Drug Discovery
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Evaluation of pharmacological properties of Danshen compounds by computational and biochemical approaches
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MetStabOn—Online Platform for Metabolic Stability Predictions
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Confident Application of a Global Human Liver Microsomal Activity QSAR
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Computational Tools And Resources For Metabolism-Related Property Predictions. 2. Application To Prediction Of Half-Life Time In Human Liver Microsomes
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How to Explain Individual Classification Decisions
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