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A Scoring Scheme for Discriminating between Drugs and Nondrugs

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Reactive compounds and in vitro false positives in HTS
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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
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Review of ring perception algorithms for chemical graphs

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

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Compound Selection
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Chemoinformatics
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Cheminformatics
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QSAR
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Combinatorial Library Design, Molecular Similarity, and Diversity Applications
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Virtual Screening
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Selecting the right compounds for screening: does Lipinski's Rule of 5 for pharmaceuticals apply to agrochemicals?
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Appendix
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The Drug Discovery Process
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Computer‐Aided Molecular Diversity Analysis and Combinatorial Library Design
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Virtual Screening in Drug Discovery
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Role of Natural Products in Drug Discovery
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In SilicoMethods for the Analysis of Metabolites and Drug Molecules
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In SilicoADME/Tox Predictions
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Maximizing the Power of a Local Model for ADMET‐Property Prediction
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In Silico Prediction Models for Blood‐Brain Barrier Permeation
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Library Filtering Systems and Prediction of Drug‐Like Properties
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Database Profiling by Neural Networks
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Design and Diversity Analysis of Compound Libraries for Lead Discovery
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Computer‐Assisted Library Design
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Chemoinformatics and the Quest for Leads in Drug Discovery
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Molecular Diversity
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Docking and Scoring for Structure‐based Drug Design
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Lead Identification by Virtual Screening
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Bioinformatics: From Peptides to Profiled Leads
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Physicochemical Properties in Drug Profiling
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Combining high-throughput pharmacokinetic screens at the hits-to-leads stage of drug discovery
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The in silico world of virtual libraries
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Ligand efficiency indices as guideposts for drug discovery
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Nonleadlikeness and leadlikeness in biochemical screening
inferred← citeshttps://doi.org/10.1016/s1367-5931(00)00089-2openalex-snapshot
Optimization of chemical libraries by neural networks
inferred← citeshttps://doi.org/10.1016/s1367-5931(00)00219-2openalex-snapshot
Visual and computational analysis of structure–activity relationships in high-throughput screening data
inferred← citeshttps://doi.org/10.1016/s1367-5931(99)80058-1openalex-snapshot
Recognizing molecules with drug-like properties
inferred← citeshttps://doi.org/10.1016/s1477-3627(02)02274-2openalex-snapshot
Integrating computer-based de novo drug design and multidimensional filtering for desirable drugs
inferred← citeshttps://doi.org/10.1016/s1572-557x(05)02001-5openalex-snapshot
Reverse pharmacognosy: a new concept for accelerating natural drug discovery
inferred← citeshttps://doi.org/10.1016/s1574-1400(06)02008-1openalex-snapshot
Chapter 8 Machine Learning in Computational Chemistry
inferred← citeshttps://doi.org/10.1016/s1574-1400(06)02009-3openalex-snapshot
Chapter 9 Molecular Similarity: Advances in Methods, Applications and Validations in Virtual Screening and QSAR
inferred← citeshttps://doi.org/10.1016/s1741-8364(04)02402-3openalex-snapshot
Using artificial neural networks to drive virtual screening of combinatorial libraries
inferred← citeshttps://doi.org/10.1016/s1877-1173(10)91006-5openalex-snapshot
The T1R2/T1R3 Sweet Receptor and TRPM5 Ion Channel
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01373openalex-snapshot
Using Machine Learning To Predict Partition Coefficient (Log P ) and Distribution Coefficient (Log D ) with Molecular Descriptors and Liquid Chromatography Retention Time
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c03088openalex-snapshot
Temporally Validated Models for Target “Tclin-likeness”
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b01162openalex-snapshot
Deep Learning-Based Imbalanced Data Classification for Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.4c02179openalex-snapshot
Rethinking Medicinal Chemistry in the Cheminformatics Age
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.4c03078openalex-snapshot
Tertiary Alcohol: Reaping the Benefits but Minimizing the Drawbacks of Hydroxy Groups in Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.9b01476openalex-snapshot
Application of Negative Design To Design a More Desirable Virtual Screening Library
inferred← citeshttps://doi.org/10.1021/cc0000388openalex-snapshot
Chemography: The Art of Navigating in Chemical Space
inferred← citeshttps://doi.org/10.1021/cc000073eopenalex-snapshot
Diverse Viewpoints on Computational Aspects of Molecular Diversity
inferred← citeshttps://doi.org/10.1021/cc000097lopenalex-snapshot
Scaffold Architecture and Pharmacophoric Properties of Natural Products and Trade Drugs: Application in the Design of Natural Product-Based Combinatorial Libraries
inferred← citeshttps://doi.org/10.1021/cc010013oopenalex-snapshot
Solid-Phase Synthesis of Libraries Generated from a 4-Phenyl-2-carboxy-piperazine Scaffold
inferred← citeshttps://doi.org/10.1021/cc010093wopenalex-snapshot
Pharmacokinetically Based Mapping Device for Chemical Space Navigation
inferred← citeshttps://doi.org/10.1021/cc020081qopenalex-snapshot
Amino Acid-Derived Heterocycles as Combinatorial Library Targets: Spirocyclic Ketal Lactones
inferred← citeshttps://doi.org/10.1021/cc049866hopenalex-snapshot
Focused Combinatorial Library Design Based on Structural Diversity, Druglikeness and Binding Affinity Score
inferred← citeshttps://doi.org/10.1021/cc990032mopenalex-snapshot
Toward Designing Drug-Like Libraries: A Novel Computational Approach for Prediction of Drug Feasibility of Compounds
inferred← citeshttps://doi.org/10.1021/ci000111hopenalex-snapshot
Structural Diversity of Small Molecule Libraries
inferred← citeshttps://doi.org/10.1021/ci0001482openalex-snapshot
Selected Concepts and Investigations in Compound Classification, Molecular Descriptor Analysis, and Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci000150topenalex-snapshot
Comparison of the NCI Open Database with Seven Large Chemical Structural Databases
inferred← citeshttps://doi.org/10.1021/ci000321uopenalex-snapshot
Variability of Molecular Descriptors in Compound Databases Revealed by Shannon Entropy Calculations
inferred← citeshttps://doi.org/10.1021/ci0003303openalex-snapshot
Distinguishing between Natural Products and Synthetic Molecules by Descriptor Shannon Entropy Analysis and Binary QSAR Calculations
inferred← citeshttps://doi.org/10.1021/ci0003348openalex-snapshot
Results of a New Classification Algorithm Combining K Nearest Neighbors and Recursive Partitioning
inferred← citeshttps://doi.org/10.1021/ci0003810openalex-snapshot
Improving the Odds in Discriminating “Drug-like” from “Non Drug-like” Compounds
inferred← citeshttps://doi.org/10.1021/ci000397qopenalex-snapshot
Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination
inferred← citeshttps://doi.org/10.1021/ci000403iopenalex-snapshot
Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery
inferred← citeshttps://doi.org/10.1021/ci010072popenalex-snapshot
Computing Wiener-Type Indices for Virtual Combinatorial Libraries Generated from Heteroatom-Containing Building Blocks
inferred← citeshttps://doi.org/10.1021/ci0102721openalex-snapshot
Prediction of CNS Activity of Compound Libraries Using Substructure Analysis
inferred← citeshttps://doi.org/10.1021/ci0102867openalex-snapshot
Differential Shannon Entropy as a Sensitive Measure of Differences in Database Variability of Molecular Descriptors
inferred← citeshttps://doi.org/10.1021/ci010366aopenalex-snapshot
Is There a Difference between Leads and Drugs? A Historical Perspective
inferred← citeshttps://doi.org/10.1021/ci010379oopenalex-snapshot
Neural Network Studies. 4. Introduction to Associative Neural Networks
inferred← citeshttps://doi.org/10.1021/ci0200467openalex-snapshot
Property Distributions: Differences between Drugs, Natural Products, and Molecules from Combinatorial Chemistry
inferred← citeshttps://doi.org/10.1021/ci020267copenalex-snapshot
Selecting Screening Candidates for Kinase and G Protein-Coupled Receptor Targets Using Neural Networks
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