Fragment-based QSAR: perspectives in drug design (Q37382159)

20 August 2017

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title

Fragment-based QSAR: perspectives in drug design (English)

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20 August 2017

Lívia B Salum

1

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PubMed ID

19184499

20 August 2017

Adriano D Andricopulo

series ordinal

2

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20 August 2017

31 January 2009

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20 August 2017

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20 August 2017

13

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20 August 2017

3

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20 August 2017

277-285

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https://scigraph.springernature.com/pub.10.1007/s11030-009-9112-5

20 August 2017

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A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds.

21 January 2018

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https://api.crossref.org/works/10.1007%2FS11030-009-9112-5

21 January 2018

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Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies

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Hologram quantitative structure-activity relationships for a series of farnesoid X receptor activators

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Structural basis for selective inhibition of trypanosomatid glyceraldehyde-3-phosphate dehydrogenase: molecular docking and 3D QSAR studies.

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Virtual screening for aryl hydrocarbon receptor binding prediction

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Use of 3D QSAR models for database screening: a feasibility study.

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Two-dimensional QSAR studies on arylpiperazines as high-affinity 5-HT(1A) receptor ligands.

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The price of innovation: new estimates of drug development costs

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Two- and three-dimensional quantitative structure-activity relationships for a series of purine nucleoside phosphorylase inhibitors.

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Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents

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3D QSAR comparative molecular field analysis on nonsteroidal farnesoid X receptor activators

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QSAR studies on piperazinylalkylisoxazole analogues selectively acting on dopamine D3 receptor by HQSAR and CoMFA.

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Hologram QSAR model for the prediction of human oral bioavailability

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2D Quantitative structure-activity relationship studies on a series of cholesteryl ester transfer protein inhibitors

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Structure-activity relationships of the antimalarial agent artemisinin. 6. The development of predictive in vitro potency models using CoMFA and HQSAR methodologies

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Hologram quantitative structure-activity relationships investigations of non-nucleoside reverse transcriptase inhibitors

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12 December 2020

Predictive QSAR modeling workflow, model applicability domains, and virtual screening

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12 December 2020

High-throughput and virtual screening: core lead discovery technologies move towards integration

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12 December 2020

Hit and lead generation: beyond high-throughput screening

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12 December 2020

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12 December 2020

Chem-bioinformatics and QSAR: a review of QSAR lacking positive hydrophobic terms

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In Silico ADME Prediction: Data, Models, Facts and Myths

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12 December 2020

A comparative study of quantitative structure-activity relationship methods based on gallic acid derivatives.

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12 December 2020

10.1007/S11030-009-9112-5

Identifiers

DOI

1 reference

20 August 2017

1 reference

PubMed ID

19184499