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In Silico Categorization of in Vivo Intrinsic Clearance Using Machine Learning

https://doi.org/10.1021/mp300484r
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cem.695openalex-snapshot
Orthogonal projections to latent structures (O‐PLS)
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Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity
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Data Mining for Imbalanced Datasets: An Overview
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The influence of polarization functions on molecular orbital hydrogenation energies
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Data Mining: Practical Machine Learning Tools and Techniques
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Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design
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Physicochemical Space for Optimum Oral Bioavailability: Contribution of Human Intestinal Absorption and First-Pass Elimination
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Self—Consistent Molecular Orbital Methods. XII. Further Extensions of Gaussian—Type Basis Sets for Use in Molecular Orbital Studies of Organic Molecules
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Determining the best animal model for human cytochrome P450 activities: a comparison of mouse, rat, rabbit, dog, micropig, monkey and man
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Utility of metabolic stability screening: comparison ofin vitroandin vivoclearance
inferredcites →https://doi.org/10.1080/00498250802446286openalex-snapshot
Reliability of human cryopreserved hepatocytes and liver microsomes asin vitrosystems to predict metabolic clearance
inferredcites →https://doi.org/10.1103/physreva.38.3098openalex-snapshot
Density-functional exchange-energy approximation with correct asymptotic behavior
inferredcites →https://doi.org/10.1103/physrevb.33.8822openalex-snapshot
Density-functional approximation for the correlation energy of the inhomogeneous electron gas
inferredcites →https://doi.org/10.1124/dmd.32.6.603openalex-snapshot
A COMPREHENSIVE QUANTITATIVE AND QUALITATIVE EVALUATION OF EXTRAPOLATION OF INTRAVENOUS PHARMACOKINETIC PARAMETERS FROM RAT, DOG, AND MONKEY TO HUMANS. I. CLEARANCE
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SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.2174/156802611794480927openalex-snapshot
In-Silico ADME Models: A General Assessment of their Utility in Drug Discovery Applications

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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The role of intramolecular self‐destruction of reactive metabolic intermediates in determining toxicity
inferred← citeshttps://doi.org/10.1007/978-3-319-56850-8_11openalex-snapshot
In Silico Approaches for the Prediction of In Vivo Biotransformation Rates
inferred← citeshttps://doi.org/10.1007/s10928-025-09999-yopenalex-snapshot
Computational approaches for toxicology and Pharmacokinetic properties prediction
inferred← citeshttps://doi.org/10.1007/s13318-024-00883-7openalex-snapshot
Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review
inferred← citeshttps://doi.org/10.1007/s40262-024-01416-wopenalex-snapshot
Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity
inferred← citeshttps://doi.org/10.1016/j.addr.2015.03.014openalex-snapshot
Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools
inferred← citeshttps://doi.org/10.1039/c5tx00055fopenalex-snapshot
Computational toxicology, friend or foe?
inferred← citeshttps://doi.org/10.1093/toxsci/kfac101openalex-snapshot
Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling
inferred← citeshttps://doi.org/10.1155/2022/4987639openalex-snapshot
Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification
inferred← citeshttps://doi.org/10.1186/s13321-021-00557-5openalex-snapshot
FP-ADMET: a compendium of fingerprint-based ADMET prediction models
inferred← citeshttps://doi.org/10.1371/journal.pone.0319673openalex-snapshot
Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm
inferred← citeshttps://doi.org/10.1517/17425255.2015.980814openalex-snapshot
Applying machine learning techniques for ADME-Tox prediction: a review
inferred← citeshttps://doi.org/10.3390/ijms17020246openalex-snapshot
Systems Pharmacology in Small Molecular Drug Discovery
inferred← citeshttps://doi.org/10.3390/pharmaceutics15041260openalex-snapshot
Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives
inferred← citeshttps://doi.org/10.5599/admet.2772openalex-snapshot
Leveraging machine learning models in evaluating ADMET properties for drug discovery and development
inferred← citeshttps://doi.org/10.6084/m9.figshare.16696290openalex-snapshot
Additional file 1 of FP-ADMET: a compendium of fingerprint-based ADMET prediction models
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