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Data Visualization during the Early Stages of Drug Discovery

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What this work says

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

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Elements of Information Theory
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ChemSpaceShuttle: A tool for data mining in drug discovery by classification, projection, and 3D visualization
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HTS in the new millennium
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Rational drug discovery revisited: interfacing experimental programs with bio- and chemo-informatics
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Predicting ADME properties in silico: methods and models
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High-throughput drug discovery: What can we expect from HTS?
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Computational methods for the prediction of ‘drug-likeness’
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Pharmacokinetically Based Mapping Device for Chemical Space Navigation
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Ligand-Based Combinatorial Design of Selective Purinergic Receptor (A2A) Antagonists Using Self-Organizing Maps
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Selecting Screening Candidates for Kinase and G Protein-Coupled Receptor Targets Using Neural Networks
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A Consensus Neural Network-Based Technique for Discriminating Soluble and Poorly Soluble Compounds
inferredcites →https://doi.org/10.1021/ci0203848openalex-snapshot
Recursive Median Partitioning for Virtual Screening of Large Databases
inferredcites →https://doi.org/10.1021/ci0303013openalex-snapshot
GPCR-Tailored Pharmacophore Pattern Recognition of Small Molecular Ligands
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Metric Validation and the Receptor-Relevant Subspace Concept
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A Comparison of Physiochemical Property Profiles of Development and Marketed Oral Drugs
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Deriving Knowledge through Data Mining High-Throughput Screening Data
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A Scoring Scheme for Discriminating between Drugs and Nondrugs
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Integration of virtual and high-throughput screening
inferredcites →https://doi.org/10.1038/nrg1317openalex-snapshot
Chemogenomics: an emerging strategy for rapid target and drug discovery
inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot
Neural Networks for Pattern Recognition
inferredcites →https://doi.org/10.1109/34.1000238openalex-snapshot
Hierarchical GTM: constructing localized nonlinear projection manifolds in a principled way
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A Nonlinear Mapping for Data Structure Analysis
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GTM: The Generative Topographic Mapping
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Partitioning Methods for the Identification of Active Molecules
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Comprehensive Computational Assessment of ADME Properties Using Mapping Techniques

What points at this work

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

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Multiobjective Optimization for Drug Discovery
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Chemoinformatic Characterization of the Chemical Space and Molecular Diversity of Compound Libraries
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Computational Techniques to Support Hit Triage
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Data Visualization in Pharmaceutical Development
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Nonlinear Dimensionality Reduction for Visualizing Toxicity Data: Distance‐Based Versus Topology‐Based Approaches
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Interrogating Novel Areas of Chemical Space for Drug Discovery using Chemoinformatics
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Progress in the Analysis of Multiple Activity Profile of Screening Data Using Computational Approaches
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Target‐Driven Subspace Mapping Methods and Their Applicability Domain Estimation
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Visual Interpretation of Kernel‐Based Prediction Models
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From Virtual Screening to Bioactive Compounds by Visualizing and Clustering of Chemical Space
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Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure‐Activity Modeling and Dataset Comparison
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Progress in the Visualization and Mining of Chemical and Target Spaces
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Visual Analysis of Biological Activity Data with Scaffold Hunter
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Impact of distance-based metric learning on classification and visualization model performance and structure–activity landscapes
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Predictive cartography of metal binders using generative topographic mapping
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Chemical Space: Missing Pieces in Cheminformatics
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Virtual screening
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Data Mapping: Linear Methods versus Nonlinear Techniques
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Data Mapping: Linear Methods versus Nonlinear Techniques
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Application of data mining approaches to drug delivery
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Chemotography for multi-target SAR analysis in the context of biological pathways
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Visualizing the drug target landscape
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Visualizing the drug target landscape
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Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery
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Investigating Pharmacological Similarity by Charting Chemical Space
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De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping
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Visual Characterization and Diversity Quantification of Chemical Libraries: 1. Creation of Delimited Reference Chemical Subspaces
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MQN-Mapplet: Visualization of Chemical Space with Interactive Maps of DrugBank, ChEMBL, PubChem, GDB-11, and GDB-13
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Toward Navigating Chemical Space of Ionic Liquids: Prediction of Melting Points Using Generative Topographic Maps
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Novel visualization methods for protein data
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A Visual Analytics Model Applied to Lead Generation Library Design in Drug Discovery
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Scaffold Hunter: a comprehensive visual analytics framework for drug discovery
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Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics
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