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QSAR Classification Model for Antibacterial Compounds and Its Use in Virtual Screening

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What this work says

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

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Comparison of Structure‐ and Ligand‐Based Virtual Screening Protocols Considering Hit List Complementarity and Enrichment Factors

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Modeling Discrimination between Antibacterial and Non‐Antibacterial Activity based on 3D Molecular Descriptors

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QSRR analysis of β-lactam antibiotics on a penicillin G targeted MIP stationary phase

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Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability

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Quantitative structure–activity relationship study of antitubercular fluoroquinolones

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Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity

inferredcites →https://doi.org/10.1016/j.drudis.2006.10.005openalex-snapshot

Similarity-based virtual screening using 2D fingerprints

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In silico classification of adenosine receptor antagonists using Laplacian-modified naïve Bayesian, support vector machine, and recursive partitioning

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Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods

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CHEMICAL STUDIES WITH PLEUROMUTILIN

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Artificial neural network applied to the discrimination of antibacterial activity by topological methods

inferredcites →https://doi.org/10.1016/s0169-409x(00)00129-0openalex-snapshot

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

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(02)00184-5openalex-snapshot

Discrimination and selection of new potential antibacterial compounds using simple topological descriptors

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Why do we need so many chemical similarity search methods?

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Is There a Difference between Leads and Drugs? A Historical Perspective

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Structure-Based Classification of Antibacterial Activity

inferredcites →https://doi.org/10.1021/ci030340eopenalex-snapshot

Artificial Neural Networks and Linear Discriminant Analysis: A Valuable Combination in the Selection of New Antibacterial Compounds

inferredcites →https://doi.org/10.1021/ci0342019openalex-snapshot

Designing Antibacterial Compounds through a Topological Substructural Approach

inferredcites →https://doi.org/10.1021/ci100062nopenalex-snapshot

Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments

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How Do 2D Fingerprints Detect Structurally Diverse Active Compounds? Revealing Compound Subset-Specific Fingerprint Features through Systematic Selection

inferredcites →https://doi.org/10.1021/ci300030uopenalex-snapshot

Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening

inferredcites →https://doi.org/10.1021/ci700086mopenalex-snapshot

Exploring Peptide-likeness of Active Molecules Using 2D Fingerprint Methods

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Global Bayesian Models for the Prioritization of Antitubercular Agents

inferredcites →https://doi.org/10.1021/ci800361aopenalex-snapshot

Search for Novel Aminoglycosides by Combining Fragment-Based Virtual Screening and 3D-QSAR Scoring

inferredcites →https://doi.org/10.1021/ci900309sopenalex-snapshot

Combinatorial Library Enumeration and Lead Hopping using Comparative Interaction Fingerprint Analysis and Classical 2D QSAR Methods for Seeking Novel GABAA α3 Modulators

inferredcites →https://doi.org/10.1021/jm0303195openalex-snapshot

Classification of Kinase Inhibitors Using a Bayesian Model

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Time-Related Differences in the Physical Property Profiles of Oral Drugs

inferredcites →https://doi.org/10.1021/jm700967eopenalex-snapshot

Physicochemical Properties of Antibacterial Compounds: Implications for Drug Discovery

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ADMET Evaluation in Drug Discovery. 12. Development of Binary Classification Models for Prediction of hERG Potassium Channel Blockage

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Natural Products as Sources of New Drugs over the Last 25 Years

inferredcites →https://doi.org/10.1038/nrd2201openalex-snapshot

Drugs for bad bugs: confronting the challenges of antibacterial discovery

inferredcites →https://doi.org/10.1093/jac/dkr262openalex-snapshot

Discovery research: the scientific challenge of finding new antibiotics

inferredcites →https://doi.org/10.1093/nar/gkq1126openalex-snapshot

DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs

inferredcites →https://doi.org/10.1111/j.1749-6632.2010.05828.xopenalex-snapshot

Challenges of antibacterial discovery revisited

inferredcites →https://doi.org/10.1177/1087057105281365openalex-snapshot

Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up

inferredcites →https://doi.org/10.1186/1758-2946-2-11openalex-snapshot

Predicting cytotoxicity from heterogeneous data sources with Bayesian learning

inferredcites →https://doi.org/10.1186/1758-2946-2-s1-o7openalex-snapshot

Quantifying model errors using similarity to training data

inferredcites →https://doi.org/10.7164/antibiotics.40.761openalex-snapshot

A21978C, a complex of new acidic peptide antibiotics. Isolation, chemistry, and mass spectral structure elucidation.

What points at this work

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

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