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Separating Drugs from Nondrugs: A Statistical Approach Using Atom Pair Distributions
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s00894-004-0186-9openalex-snapshot
Impact of descriptor vector scaling on the classification of drugs and nondrugs with artificial neural networks
inferredcites →https://doi.org/10.1016/s0169-409x(96)00423-1openalex-snapshot
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
inferredcites →https://doi.org/10.1016/s1093-3263(01)00103-6openalex-snapshot
General topological patterns of known drugs
inferredcites →https://doi.org/10.1016/s1093-3263(98)80008-9openalex-snapshot
DISSIM: a program for the analysis of chemical diversity
inferredcites →https://doi.org/10.1021/cc9800071openalex-snapshot
A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases
inferredcites →https://doi.org/10.1021/ci0003810openalex-snapshot
Improving the Odds in Discriminating “Drug-like” from “Non Drug-like” Compounds
inferredcites →https://doi.org/10.1021/ci00046a002openalex-snapshot
Atom pairs as molecular features in structure-activity studies: definition and applications
inferredcites →https://doi.org/10.1021/ci00046a003openalex-snapshot
Computer code for producing Eh-pH plots of equilibrium chemical systems
inferredcites →https://doi.org/10.1021/ci00054a008openalex-snapshot
Topological torsion: a new molecular descriptor for SAR applications. Comparison with other descriptors
inferredcites →https://doi.org/10.1021/ci00063a006openalex-snapshot
Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics
inferredcites →https://doi.org/10.1021/ci00063a007openalex-snapshot
Review of ring perception algorithms for chemical graphs
inferredcites →https://doi.org/10.1021/ci0302862openalex-snapshot
Drugs and Nondrugs: An Effective Discrimination with Topological Methods and Artificial Neural Networks
inferredcites →https://doi.org/10.1021/ci034043lopenalex-snapshot
Development of a Method for Evaluating Drug-Likeness and Ease of Synthesis Using a Data Set in Which Compounds Are Assigned Scores Based on Chemists' Intuition
inferredcites →https://doi.org/10.1021/ci0341161openalex-snapshot
Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
inferredcites →https://doi.org/10.1021/ci990266topenalex-snapshot
Potential Drugs and Nondrugs: Prediction and Identification of Important Structural Features
inferredcites →https://doi.org/10.1021/ci990423oopenalex-snapshot
Strategic Pooling of Compounds for High-Throughput Screening
inferredcites →https://doi.org/10.1021/jm0010670openalex-snapshot
Discriminating between Drugs and Nondrugs by Prediction of Activity Spectra for Substances (PASS)
inferredcites →https://doi.org/10.1021/jm011027bopenalex-snapshot
Descriptors, Physical Properties, and Drug-Likeness
inferredcites →https://doi.org/10.1021/jm015507eopenalex-snapshot
Simple Selection Criteria for Drug-like Chemical Matter
inferredcites →https://doi.org/10.1021/jm9602928openalex-snapshot
The Properties of Known Drugs. 1. Molecular Frameworks
inferredcites →https://doi.org/10.1021/jm970666copenalex-snapshot
Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?
inferredcites →https://doi.org/10.1021/jm9706776openalex-snapshot
A Scoring Scheme for Discriminating between Drugs and Nondrugs
inferredcites →https://doi.org/10.1021/jm9903996openalex-snapshot
Properties of Known Drugs. 2. Side Chains
inferredcites →https://doi.org/10.1023/a:1008130001697openalex-snapshot
Property distribution of drug-related chemical databases*
inferredcites →https://doi.org/10.1038/nrd1032openalex-snapshot
ADMET in silico modelling: towards prediction paradise?
inferredcites →https://doi.org/10.1073/pnas.87.6.2264openalex-snapshot
Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes.
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9783527677016.ch13openalex-snapshot
Construction of Drug‐Like Compounds by Markov Chains
inferred← citeshttps://doi.org/10.1002/minf.201100069openalex-snapshot
Selecting Relevant Descriptors for Classification by Bayesian Estimates: A Comparison with Decision Trees and Support Vector Machines Approaches for Disparate Data Sets
inferred← citeshttps://doi.org/10.1016/b978-0-12-417205-0.00015-8openalex-snapshot
Compound Properties and their Influence on Drug Quality
inferred← citeshttps://doi.org/10.1016/b978-1-68108-198-4.50012-6openalex-snapshot
Recent Advances in the Assessment of Druglikeness Using 2D-Structural Descriptors
inferred← citeshttps://doi.org/10.1016/j.drudis.2007.11.007openalex-snapshot
Assessing drug-likeness – what are we missing?
inferred← citeshttps://doi.org/10.1016/j.jmgm.2016.12.019openalex-snapshot
First protein drug target’s appraisal of lead-likeness descriptors to unfold the intervening chemical space
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00757openalex-snapshot
Conservation and Relevance of Pharmacophore Point Types
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5b00886openalex-snapshot
OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands
inferred← citeshttps://doi.org/10.1021/bk-2011-1076.ch008openalex-snapshot
In SilicoFragment-Based Generation of Drug-Like Compounds
inferred← citeshttps://doi.org/10.1021/ci700107yopenalex-snapshot
A Large Descriptor Set and a Probabilistic Kernel-Based Classifier Significantly Improve Druglikeness Classification
inferred← citeshttps://doi.org/10.1021/ci700351yopenalex-snapshot
Gradual in Silico Filtering for Druglike Substances
inferred← citeshttps://doi.org/10.1021/ci8003418openalex-snapshot
Bioisosteric Similarity of Molecules Based on Structural Alignment and Observed Chemical Replacements in Drugs
inferred← citeshttps://doi.org/10.1021/ci9000458openalex-snapshot
FOG: Fragment Optimized Growth Algorithm for the de Novo Generation of Molecules Occupying Druglike Chemical Space
inferred← citeshttps://doi.org/10.1021/ci9004354openalex-snapshot
Predicting Oral Druglikeness by Iterative Stochastic Elimination
inferred← citeshttps://doi.org/10.1021/co2000168openalex-snapshot
Atom Type Preferences, Structural Diversity, and Property Profiles of Known Drugs, Leads, and Nondrugs: A Comparative Assessment
inferred← citeshttps://doi.org/10.1080/08927022.2022.2060968openalex-snapshot
Structure-based virtual screening, ADMET profiling, and molecular dynamics simulation studies on HIV-1 protease for identification of active phytocompounds as potential anti-HIV agents
inferred← citeshttps://doi.org/10.1186/1758-2946-4-31openalex-snapshot
Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines
inferred← citeshttps://doi.org/10.1517/17460441.2010.497534openalex-snapshot
De novodesign: balancing novelty and confined chemical space
inferred← citeshttps://doi.org/10.1517/17460441.2012.648612openalex-snapshot
Going further than Lipinski's rule in drug design
inferred← citeshttps://doi.org/10.2751/jcac.9.70openalex-snapshot
Development of Drug-likeness Model and Its Visualization
inferred← citeshttps://doi.org/10.70675/763f9808zf2fez469ezba2cz56622b867580openalex-snapshot
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