Literature DB >> 22823126

Fragment-based QSAR strategies in drug design.

Lívia B Salum1, Adriano D Andricopulo.   

Abstract

Recently, fragment-based drug design has been established as a crucial strategy for hit identification and lead generation, which has strongly encouraged the development of approaches to specifically recognize and evaluate molecular fragments or structural scaffolds that preferentially interact with particular sites of important biological targets. In this context, fragment-based quantitative structure-activity relationship (FB-QSAR) has emerged as a versatile tool to explore the chemical and biological space of data sets of compounds. FB-QSAR approaches have evolved from a classical use in the generation of standard QSAR models into advanced drug design tools for database mining, pharmacokinetic property prediction and optimization of multiple parameters. This paper provides a brief perspective on the evolution and current status of FB-QSAR, highlighting new opportunities in drug design.

Year:  2010        PMID: 22823126     DOI: 10.1517/17460441003782277

Source DB:  PubMed          Journal:  Expert Opin Drug Discov        ISSN: 1746-0441            Impact factor:   6.098


  3 in total

1.  Fragment-guided approach to incorporating structural information into a CoMFA study: BACE-1 as an example.

Authors:  Lívia Barros Salum; Napoleão Fonseca Valadares
Journal:  J Comput Aided Mol Des       Date:  2010-07-27       Impact factor: 3.686

2.  Distributed Representation of Chemical Fragments.

Authors:  Suman K Chakravarti
Journal:  ACS Omega       Date:  2018-03-08

3.  Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks.

Authors:  Suman K Chakravarti; Sai Radha Mani Alla
Journal:  Front Artif Intell       Date:  2019-09-06
  3 in total

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