在线客服系统
 
招聘信息  |  中文版  |  ENGLISH
    首页 > 产品
    快速导航
  重点推荐
  GastroPlus药代及制剂模拟软件
  ADMET性质预测 ADMET Predictor
  体外溶出模拟平台DDDPlus
  药物筛选、设计、优化
  数据挖掘与药物设计软件
  化学结构绘制软件-免费
  药物制剂
  制剂体外溶出模拟平台
  GastroPlus药代及制剂模拟软件
  安全评价与毒代研究
  毒性与代谢产物预测软件
  DMPK与临床药理
  生理药动PBPK软件
  仿制药一致性评价
  定制开发产品
  实验试剂及耗材
 
MedChem Studio药物设计及数据挖掘软件

  说明: http://www.pharmogo.com/upload/1-2.png 说明: http://www.pharmogo.com/upload/modl(8).png   说明: http://www.pharmogo.com/upload/1-5.png  

采用MedChem Studio发表的部分文献

Design of a General-Purpose European Compound Screening Library for EU-OPENSCREEN.
Horvath D, Lisurek M, Rupp B, Kühne R, Specker E, von Kries J, Rognan D, Andersson CD, Almqvist F, Elofsson M, Enqvist PA, Gustavsson AL, Remez N, Mestres J, Marcou G, Varnek A, Hibert M, Quintana J, Frank R. (2014) ChemMedChem. doi: 10.1002/cmdc.201402126

A Computational Drug-Target Network for Yuanhu Zhitong Prescription.
Xu H, Tao Y, Lu P, Wang P, Zhang F, Yuan Y, Wang S, Xiao X, Yang H, Huang L. (2013) Evidence-Based Complementary and Alternative Medicine, Article ID 658531

A High-Throughput Screen against Pantothenate Synthetase (PanC) Identifies 3-Biphenyl-4-Cyanopyrrole-2-Carboxylic Acids as a New Class of Inhibitor with Activity against Mycobacterium tuberculosis.
Kumar A, Casey A, Odingo J, Kesicki EA, Abrahams G, Vieth M, Masquelin T, Mizrahi V, Hipskind PA, Sherman DR, Parish T. (2013) PLOS ONE 8(1):1-8

Lions and tigers and bears, oh my! Three barriers to progress in computer-aided molecular design.
Clark RD, Waldman M. (2012) J. Computer-Aided Molecular Design 26(1), 29-34

Trends in kinase selectivity: insights for target class-focused library screening.
Posy SL, Hermsmeier MA, Vaccaro W, Ott K-H, Todderud G, Lippy JS, Trainor GL, Loughney DA, Johnson SR. (2011) J. Med. Chem. 54(1), pp 54-66

Are target-family-privileged substructures truly privileged?
Schnur DM, Hermsmeier MA, Tebben AJ. (2006) J. Med. Chem. 49(6), pp 2000–09

NIPALSTREE: A new hierarchical clustering approach for large compound libraries and its application to virtual screening.
Böcker A, Schneider G, Teckentrup A. (2006) J. Chem. Inf. Model. 46(6), pp 2220–29

Assessing the scaffold diversity of screening libraries.
Krier M, Bret G, Rognan D. (2006) J. Chem. Inf. Model. 46(2), pp 512–24

Classifying kinase inhibitor-likeness by using machine-learning.
Briem H, Gunther J. (2005) ChemBioChem, 6, 558-66

Solubility prediction by recursive partitioning.
Xia X, Maliski E, Cheetham J, Poppe L. (2003) Pharm. Res. 20:1634-40

Analysis of large screening data sets via adaptively grown phylogenetic-like trees.
Nicolaou CA, Tamura SY, Kelley BP, Bassett SI, Nutt RF. (2002) J. Chem. Inf. Comput. Sci. 42(5): 1069-79

Rule extraction from a mutagenicity data set using adaptively grown phylogenetic-like trees.
Bacha PA, Gruver HS, Den Hartog BK, Tamura SY, Nutt RF. (2002) J. Chem. Inf. Comput. Sci. 42(5): 1104-11

Data analysis of high-throughput screening results: application of multidomain clustering to the NCI anti-HIV data set.
Tamura SY, Bacha PA, Gruver HS, Nutt RF. (2002) J. Med. Chem. 45(14): 3082-93

Ties in proximity and clustering compounds.
MacCuish JD, Nicolaou CA, MacCuish NJ. (2001) J. Chem. Inf. Comput. Sci. 41: 134-46

Results of a new classification algorithm combining K nearest neighbors and recursive partitioning.

 

上一页 

 

第    1      2      3      4      5    页