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Directed R-Group Combination Graph: A Methodology To Uncover Structure–Activity Relationship Patterns in a Series of Analogues

https://doi.org/10.1021/jm201362h

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What this work says

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

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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

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Single R-Group Polymorphisms (SRPs) and R-Cliffs: An Intuitive Framework for Analyzing and Visualizing Activity Cliffs in a Single Analog Series

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SAR Maps: A New SAR Visualization Technique for Medicinal Chemists

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Activity Landscape Representations for Structure−Activity Relationship Analysis

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Exploration of Structure−Activity Relationship Determinants in Analogue Series

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The Properties of Known Drugs. 1. Molecular Frameworks

What points at this work

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Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function

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Sharing Chemical Relationships Does Not Reveal Structures

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AnalogExplorer: A New Method for Graphical Analysis of Analog Series and Associated Structure–Activity Relationship Information

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Systematic assessment of analog relationships between bioactive compounds and promiscuity of analog sets

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AnalogExplorer2 – Stereochemistry sensitive graphical analysis of large analog series

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Modeling of activity landscapes for drug discovery

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Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

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New methods of multiscale chemical space analysis : visualization of structure-activity relationships and structural pattern extraction

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