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Distinguishing between Bioactive and Modeled Compound Conformations through Mining of Emerging Chemical Patterns
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/1098-1128(200103)21:2<105::aid-med1002>3.0.co;2-uopenalex-snapshot
Adenosine deaminase: Functional implications and different classes of inhibitors
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Optimization of parameters for semiempirical methods I. Method
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MOPAC: A semiempirical molecular orbital program
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Conformational changes of small molecules binding to proteins
inferredcites →https://doi.org/10.1016/j.jmb.2005.12.012openalex-snapshot
Conformational Diversity of Ligands Bound to Proteins
inferredcites →https://doi.org/10.1021/ci600301topenalex-snapshot
Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection
inferredcites →https://doi.org/10.1021/ci6005454openalex-snapshot
Conformational Sampling of Bioactive Molecules: A Comparative Study
inferredcites →https://doi.org/10.1021/ci7001473openalex-snapshot
2D Depiction of Protein−Ligand Complexes
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Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model
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Ground states of molecules. 38. The MNDO method. Approximations and parameters
inferredcites →https://doi.org/10.1021/ja038606lopenalex-snapshot
A Highly Potent Non-Nucleoside Adenosine Deaminase Inhibitor: Efficient Drug Discovery by Intentional Lead Hybridization
inferredcites →https://doi.org/10.1021/jm030563wopenalex-snapshot
Conformational Analysis of Drug-Like Molecules Bound to Proteins: An Extensive Study of Ligand Reorganization upon Binding
inferredcites →https://doi.org/10.1021/jm030580lopenalex-snapshot
The PDBbind Database: Collection of Binding Affinities for Protein−Ligand Complexes with Known Three-Dimensional Structures
inferredcites →https://doi.org/10.1021/jm048957qopenalex-snapshot
The PDBbind Database: Methodologies and Updates
inferredcites →https://doi.org/10.1023/a:1008007507641openalex-snapshot
Conformational energy penalties of protein-bound ligands
inferredcites →https://doi.org/10.1023/a:1016320106741openalex-snapshot
Can we separate active from inactive conformations?
inferredcites →https://doi.org/10.1145/312129.312191openalex-snapshot
Efficient mining of emerging patterns
inferredcites →https://doi.org/10.2174/157340608783331452openalex-snapshot
Simulation of Sequential Screening Experiments Using Emerging Chemical Patterns
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9783527655984.ch10openalex-snapshot
Applications of Rule‐Based Methods to Data Mining of Polypharmacology Data Sets
inferred← citeshttps://doi.org/10.1002/chin.200851210openalex-snapshot
ChemInform Abstract: Distinguishing Between Bioactive and Modeled Compound Conformations Through Mining of Emerging Chemical Patterns.
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Computational Multi-Target Drug Design
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Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble
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Bioactive focus in conformational ensembles: a pluralistic approach
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Emerging chemical patterns applied to prediction of P-glycoprotein inhibitors
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Prediction and characterization of P-glycoprotein substrates potentially bound to different sites by emerging chemical pattern and hierarchical cluster analysis
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Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00259openalex-snapshot
Toward Focusing Conformational Ensembles on Bioactive Conformations: A Molecular Mechanics/Quantum Mechanics Study
inferred← citeshttps://doi.org/10.1021/ci100092xopenalex-snapshot
Introduction of Jumping Fragments in Combination with QSARs for the Assessment of Classification in Ecotoxicology
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A Multivariate Chemical Similarity Approach to Search for Drugs of Potential Environmental Concern
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Classification of Compounds with Distinct or Overlapping Multi-Target Activities and Diverse Molecular Mechanisms Using Emerging Chemical Patterns
inferred← citeshttps://doi.org/10.1021/ci400597dopenalex-snapshot
Prediction of Individual Compounds Forming Activity Cliffs Using Emerging Chemical Patterns
inferred← citeshttps://doi.org/10.1021/ci500147bopenalex-snapshot
Prediction of Compounds in Different Local Structure–Activity Relationship Environments Using Emerging Chemical Patterns
inferred← citeshttps://doi.org/10.1021/ci900035zopenalex-snapshot
Mining Statistically Significant Molecular Substructures for Efficient Molecular Classification
inferred← citeshttps://doi.org/10.1038/s41597-022-01261-1openalex-snapshot
AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials
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Novel arylβ-aminocarbonyl derivatives as inhibitors ofTrypanosoma cruzitrypanothione reductase: binding mode revised by docking and GRIND2-based 3D-QSAR procedures
inferred← citeshttps://doi.org/10.1109/jbhi.2019.2939149openalex-snapshot
Exploratory Data Mining for Subgroup Cohort Discoveries and Prioritization
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CREDO: A Protein–Ligand Interaction Database for Drug Discovery
inferred← citeshttps://doi.org/10.1186/1471-2105-11-545openalex-snapshot
Bioactive conformational generation of small molecules: A comparative analysis between force-field and multiple empirical criteria based methods
inferred← citeshttps://doi.org/10.1186/s13321-023-00794-wopenalex-snapshot
Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
inferred← citeshttps://doi.org/10.1517/17460441.2010.513711openalex-snapshot
Biasing conformational ensembles towards bioactive-like conformers for ligand-based drug design
inferred← citeshttps://doi.org/10.2200/s00897ed1v01y201901dmk016openalex-snapshot
Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems
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Additional file 1 of Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
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