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Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms

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What this work says

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

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Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents
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The Nature of Statistical Learning Theory
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Application of Free–Wilson Selectivity Analysis for Combinatorial Library Design
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Composite multi-parameter ranking of real and virtual compounds for design of MC4R agonists: Renaissance of the Free-Wilson methodology
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Quantitative structure–pharmacokinetic/pharmacodynamic relationships
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Making medicinal chemistry more effective—application of Lean Sigma to improve processes, speed and quality
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Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle
inferredcites →https://doi.org/10.1016/j.ejmech.2010.01.002openalex-snapshot
Support vector machines: Development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives
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A rapid computational filter for predicting the rate of human renal clearance
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Learning to improve the decision-making process in research
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Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets
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The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies
inferredcites →https://doi.org/10.1021/ci049965iopenalex-snapshot
Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression
inferredcites →https://doi.org/10.1021/ci100050topenalex-snapshot
Extended-Connectivity Fingerprints
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Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data
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Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins
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The Correlation of Biological Activity of Plant Growth Regulators and Chloromycetin Derivatives with Hammett Constants and Partition Coefficients
inferredcites →https://doi.org/10.1021/jm00230a013openalex-snapshot
Quantitative structure-activity relationships. 3. A comparison of different Free-Wilson models
inferredcites →https://doi.org/10.1021/jm00334a001openalex-snapshot
A Mathematical Contribution to Structure-Activity Studies
inferredcites →https://doi.org/10.1021/jm1002793openalex-snapshot
Discovery of Imidazo[1,5-a]pyrido[3,2-e]pyrazines as a New Class of Phosphodiesterase 10A Inhibitiors
inferredcites →https://doi.org/10.1021/jm201503uopenalex-snapshot
Maximizing Lipophilic Efficiency: The Use of Free-Wilson Analysis in the Design of Inhibitors of Acetyl-CoA Carboxylase
inferredcites →https://doi.org/10.1021/jm3013163openalex-snapshot
Free-Wilson and Structural Approaches to Co-optimizing Human and Rodent Isoform Potency for 11β-Hydroxysteroid Dehydrogenase Type 1 (11β-HSD1) Inhibitors
inferredcites →https://doi.org/10.1021/jm8009525openalex-snapshot
Additivity in the Analysis and Design of HIV Protease Inhibitors
inferredcites →https://doi.org/10.1021/jm801070qopenalex-snapshot
Assessment of Additive/Nonadditive Effects in Structure−Activity Relationships: Implications for Iterative Drug Design
inferredcites →https://doi.org/10.1021/jm9911175openalex-snapshot
Nuclear Receptor−DNA Binding Specificity: A COMBINE and Free−Wilson QSAR Analysis
inferredcites →https://doi.org/10.1038/194178b0openalex-snapshot
Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients
inferredcites →https://doi.org/10.1038/nrd3078openalex-snapshot
How to improve R&D productivity: the pharmaceutical industry's grand challenge
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1145/1961189.1961199openalex-snapshot
LIBSVM
inferredcites →https://doi.org/10.1186/1758-2946-3-11openalex-snapshot
Interpreting linear support vector machine models with heat map molecule coloring
inferredcites →https://doi.org/10.1186/1758-2946-3-28openalex-snapshot
AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment
inferredcites →https://doi.org/10.2174/092986709787002736openalex-snapshot
In Silico Prediction of Drug Properties
inferredcites →https://doi.org/10.2174/138161207780765954openalex-snapshot
Artificial Intelligence Approaches for Rational Drug Design and Discovery
inferredcites →https://doi.org/10.2174/138955706776361501openalex-snapshot
Prediction of Compounds with Specific Pharmacodynamic, Pharmacokinetic or Toxicological Property by Statistical Learning Methods
inferredcites →https://doi.org/10.2174/138955707782331696openalex-snapshot
Regression Methods for Developing QSAR and QSPR Models to Predict Compounds of Specific Pharmacodynamic, Pharmacokinetic and Toxicological Properties

What points at this work

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

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The Signature Molecular Descriptor in Molecular Design
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Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification
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Explainable artificial intelligence for targeted protein degradation predictions
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Chemometrics tools in QSAR/QSPR studies: A historical perspective
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A new data representation based on relative measurements and fingerprint patterns for the development of QSAR regression models
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Drug/nondrug classification using Support Vector Machines with various feature selection strategies
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Multi-QSAR approaches for investigating the relationship between chemical structure descriptors of Thiadiazole derivatives and their corrosion inhibition performance
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Structural findings of phenylindoles as cytotoxic antimitotic agents in human breast cancer cell lines through multiple validated QSAR studies
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AI-Augmented R-Group Exploration in Medicinal Chemistry
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00101openalex-snapshot
Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets
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Improved Chemical Structure–Activity Modeling Through Data Augmentation
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Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR
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Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis
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Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future
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Conformal Regression for Quantitative Structure–Activity Relationship Modeling—Quantifying Prediction Uncertainty
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Regression Modelability Index: A New Index for Prediction of the Modelability of Data Sets in the Development of QSAR Regression Models
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Differences and Relationships Between Normal and Atypical Ductal Hyperplasia, Ductal Carcinoma In Situ, and Invasive Ductal Carcinoma Tissues in the Breast Based on Raman Spectroscopy
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