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Predicting Lipophilicity of Drug‐Discovery Molecules using Gaussian Process Models

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
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Neural Networks: Tricks of the Trade
inferredcites →https://doi.org/10.1021/ci000459copenalex-snapshot
Quantitative Structure−Activity Relationship Studies Using Gaussian Processes
inferredcites →https://doi.org/10.1021/ci010363yopenalex-snapshot
Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets
inferredcites →https://doi.org/10.1021/ci034255iopenalex-snapshot
Nonlinear Prediction of Quantitative Structure−Activity Relationships
inferredcites →https://doi.org/10.1021/ci049737oopenalex-snapshot
Classifying ‘Drug-likeness' with Kernel-Based Learning Methods
inferredcites →https://doi.org/10.1021/ci0504014openalex-snapshot
logD7.4 Modeling Using Bayesian Regularized Neural Networks. Assessment and Correction of the Errors of Prediction
inferredcites →https://doi.org/10.1021/ci600205gopenalex-snapshot
Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
inferredcites →https://doi.org/10.1080/10629360108035385openalex-snapshot
Gaussian Process: An Efficient Technique to Solve Quantitative Structure-Property Relationship Problems
inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot
Neural Networks for Pattern Recognition
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An introduction to kernel-based learning algorithms
inferredcites →https://doi.org/10.5555/1046920.1194909openalex-snapshot
A Unifying View of Sparse Approximate Gaussian Process Regression
inferredcites →https://doi.org/10.5860/choice.31-5500openalex-snapshot
Neural networks for pattern recognition

What points at this work

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

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Visual Interpretation of Kernel‐Based Prediction Models
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Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems
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Gaussian process: an alternative approach for QSAM modeling of peptides
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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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Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
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A deep learning approach for the blind logP prediction in SAMPL6 challenge
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Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge
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In silico study of natural antioxidants
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Prediction of P2Y12 antagonists using a novel genetic algorithm-support vector machine coupled approach
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Revealing key structural features for developing new agonists targeting δ opioid receptor: Combined machine learning and molecular modeling perspective
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Computational Analysis of Structure–Activity Relationships
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Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding
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Improved GNNs for Log D 7.4 Prediction by Transferring Knowledge from Low-Fidelity Data
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Systematic Modeling of log D7.4 Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis
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A Probabilistic Approach to Classifying Metabolic Stability
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Bias-Correction of Regression Models: A Case Study on hERG Inhibition
inferred← citeshttps://doi.org/10.1021/la3012316openalex-snapshot
Exploiting Micelle-Driven Coordination To Evaluate the Lipophilicity of Molecules
inferred← citeshttps://doi.org/10.1021/mp0700413openalex-snapshot
Machine Learning Models for Lipophilicity and Their Domain of Applicability
inferred← citeshttps://doi.org/10.1039/c0md00165aopenalex-snapshot
Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets
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Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning
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Predicting Error Bars for QSAR Models
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Novel Machine Learning Methods for Computational Chemistry
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A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest
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Amino Acid Derivatives of Ligustrazine-Oleanolic Acid as New Cytotoxic Agents
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How to Explain Individual Classification Decisions
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CompLife2007, Utrecht/NL. Day 1 and 2
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CompLife2007, Utrecht/NL. Day 1 and 2
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