Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

about

The potential role of in silico approaches to identify novel bioactive molecules from natural resources.Chemoinformatics: a perspective from an academic setting in Latin America.

P2860

Q38637348-44BB8B47-8757-49E9-81DC-C6109BBD6FEB Q47200709-1221FA04-E1D1-42CA-A289-09A2759E317D

P2860

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

Item

http://www.wikidata.org/entity/Q57832756

description

im Jahr 2017 veröffentlichter wissenschaftlicher Artikel

@de

wetenschappelijk artikel

@nl

наукова стаття, опублікована у 2017

@uk

name

Getting SMARt in drug discover ...... ultiple activity relationships

@en

Getting SMARt in drug discover ...... ultiple activity relationships

@nl

type

Item

label

Getting SMARt in drug discover ...... ultiple activity relationships

@en

Getting SMARt in drug discover ...... ultiple activity relationships

@nl

prefLabel

Getting SMARt in drug discover ...... ultiple activity relationships

@en

Getting SMARt in drug discover ...... ultiple activity relationships

@nl

P1476

Getting SMARt in drug discover ...... ultiple activity relationships

@en

P2093

Fernanda I. Saldívar-González

http://www.w3.org/2001/XMLSchema#string

Oscar Palomino-Hernández

http://www.w3.org/2001/XMLSchema#string

P2860

Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs

Shifting from the single to the multitarget paradigm in drug discovery

On Outliers and Activity Cliffs - Why QSAR Often Disappoints

Progress in the analysis of multiple activity profile of screening data using computational approaches.

Comparison of molecular fingerprint methods on the basis of biological profile data.

Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs.

Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles.

Data mining of protein-binding profiling data identifies structural modifications that distinguish selective and promiscuous compounds.

Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches.

P304

632-641

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1039/C6RA26230A

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

José L. Medina-Franco

J. Jesús Naveja

P577

2017-01-01T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

about

P2860

P2860

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P50

P577

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P50

P577