Literature DB >> 15807517

PowerMV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation.

Kejun Liu1, Jun Feng, S Stanley Young.   

Abstract

Ideally, a team of biologists, medicinal chemists and information specialists will evaluate the hits from high throughput screening. In practice, it often falls to nonmedicinal chemists to make the initial evaluation of HTS hits. Chemical genetics and high content screening both rely on screening in cells or animals where the biological target may not be known. There is a need to place active compounds into a context to suggest potential biological mechanisms. Our idea is to build an operating environment to help the biologist make the initial evaluation of HTS data. To this end the operating environment provides viewing of compound structure files, computation of basic biologically relevant chemical properties and searching against biologically annotated chemical structure databases. The benefit is to help the nonmedicinal chemist, biologist and statistician put compounds into a potentially informative biological context. Although there are several similar public and private programs used in the pharmaceutical industry to help evaluate hits, these programs are often built for computational chemists. Our program is designed for use by biologists and statisticians.

Mesh:

Year:  2005        PMID: 15807517     DOI: 10.1021/ci049847v

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  31 in total

1.  Secure analysis of distributed chemical databases without data integration.

Authors:  Alan F Karr; Jun Feng; Xiaodong Lin; Ashish P Sanil; S Stanley Young; Jerome P Reiter
Journal:  J Comput Aided Mol Des       Date:  2005-11-03       Impact factor: 3.686

2.  Exploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one derivatives as JAK inhibitors using various in silico techniques.

Authors:  Radhakrishnan S Jisha; Lilly Aswathy; Vijay H Masand; Jayant M Gajbhiye; Indira G Shibi
Journal:  In Silico Pharmacol       Date:  2017-10-12

3.  A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach.

Authors:  Yu Wang; Yanzhi Guo; Qifan Kuang; Xuemei Pu; Yue Ji; Zhihang Zhang; Menglong Li
Journal:  J Comput Aided Mol Des       Date:  2014-12-20       Impact factor: 3.686

4.  Cheminformatics models based on machine learning approaches for design of USP1/UAF1 abrogators as anticancer agents.

Authors:  Divya Wahi; Salma Jamal; Sukriti Goyal; Aditi Singh; Ritu Jain; Preeti Rana; Abhinav Grover
Journal:  Syst Synth Biol       Date:  2015-01-30

5.  KRAKENX: software for the generation of alignment-independent 3D descriptors.

Authors:  Vishwesh Venkatraman; Bjørn Kåre Alsberg
Journal:  J Mol Model       Date:  2016-03-29       Impact factor: 1.810

6.  Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets.

Authors:  Vinita Periwal; Jinuraj K Rajappan; Abdul Uc Jaleel; Vinod Scaria
Journal:  BMC Res Notes       Date:  2011-11-18

7.  Virtual screening of bioassay data.

Authors:  Amanda C Schierz
Journal:  J Cheminform       Date:  2009-12-22       Impact factor: 5.514

8.  A model-based ensembling approach for developing QSARs.

Authors:  Qianyi Zhang; Jacqueline M Hughes-Oliver; Raymond T Ng
Journal:  J Chem Inf Model       Date:  2009-08       Impact factor: 4.956

9.  Generation of quinolone antimalarials targeting the Plasmodium falciparum mitochondrial respiratory chain for the treatment and prophylaxis of malaria.

Authors:  Giancarlo A Biagini; Nicholas Fisher; Alison E Shone; Murad A Mubaraki; Abhishek Srivastava; Alisdair Hill; Thomas Antoine; Ashley J Warman; Jill Davies; Chandrakala Pidathala; Richard K Amewu; Suet C Leung; Raman Sharma; Peter Gibbons; David W Hong; Bénédicte Pacorel; Alexandre S Lawrenson; Sitthivut Charoensutthivarakul; Lee Taylor; Olivier Berger; Alison Mbekeani; Paul A Stocks; Gemma L Nixon; James Chadwick; Janet Hemingway; Michael J Delves; Robert E Sinden; Anne-Marie Zeeman; Clemens H M Kocken; Neil G Berry; Paul M O'Neill; Stephen A Ward
Journal:  Proc Natl Acad Sci U S A       Date:  2012-05-07       Impact factor: 11.205

Review 10.  QSPR studies on aqueous solubilities of drug-like compounds.

Authors:  Pablo R Duchowicz; Eduardo A Castro
Journal:  Int J Mol Sci       Date:  2009-06-03       Impact factor: 6.208

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