Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

被引：406

作者：

Cheng, Tiejun ^{[1
]}

Li, Qingliang ^{[1
]}

Zhou, Zhigang ^{[1
]}

Wang, Yanli ^{[1
]}

Bryant, Stephen H. ^{[1
]}

机构：

[1] NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, Bethesda, MD 20894 USA

来源：

AAPS JOURNAL | 2012年 / 14卷 / 01期

基金：

美国国家卫生研究院;

关键词：

docking; machine learning; structure-based virtual scoring; target-biased scoring function; EMPIRICAL SCORING FUNCTIONS; MOLECULAR DOCKING; BINDING-AFFINITY; LIGAND-BINDING; PROTEIN FLEXIBILITY; GENETIC ALGORITHM; FLEXIBLE DOCKING; CROSS-DOCKING; VALIDATION; INHIBITORS;

D O I：

10.1208/s12248-012-9322-0

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers' practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.

引用

页码：133 / 141

页数：9

共 98 条

[1] ICM - A NEW METHOD FOR PROTEIN MODELING AND DESIGN - APPLICATIONS TO DOCKING AND STRUCTURE PREDICTION FROM THE DISTORTED NATIVE CONFORMATION [J].

ABAGYAN, R ;

TOTROV, M ;

KUZNETSOV, D .

JOURNAL OF COMPUTATIONAL CHEMISTRY, 1994, 15 (05) :488-506

[2] Role of the active-site solvent in the thermodynamics of factor Xa ligand binding [J].

Abel, Robert ;

Young, Tom ;

Farid, Ramy ;

Berne, Bruce J. ;

Friesner, Richard A. .

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2008, 130 (09) :2817-2831

[3] A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming [J].

Amini, Ata ;

Shrimpton, Paul J. ;

Muggleton, Stephen H. ;

Sternberg, Michael J. E. .

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2007, 69 (04) :823-831

[4] A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking [J].

Ballester, Pedro J. ;

Mitchell, John B. O. .

BIOINFORMATICS, 2010, 26 (09) :1169-1175

[5] Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs [J].

Bohm, HJ .

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 1998, 12 (04) :309-323

[6] Four-Dimensional Docking: A Fast and Accurate Account of Discrete Receptor Flexibility in Ligand Docking [J].

Bottegoni, Giovanni ;

Kufareva, Irina ;

Totrov, Maxim ;

Abagyan, Ruben .

JOURNAL OF MEDICINAL CHEMISTRY, 2009, 52 (02) :397-406

[7] Automatic clustering of docking poses in virtual screening process using self-organizing map [J].

Bouvier, Guillaume ;

Evrard-Todeschi, Nathalie ;

Girault, Jean-Pierre ;

Bertho, Gildas .

BIOINFORMATICS, 2010, 26 (01) :53-60

[8] Structure-Based Design of Potent Aromatase Inhibitors by High-Throughput Docking [J].

Caporuscio, Fabiana ;

Rastelli, Giulio ;

Imbriano, Carol ;

Del Rio, Alberto .

JOURNAL OF MEDICINAL CHEMISTRY, 2011, 54 (12) :4006-4017

[9] Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery [J].

Catana, Cornel ;

Stouten, Pieter F. W. .

JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2007, 47 (01) :85-91

[10] Docking and high throughput docking: Successes and the challenge of protein flexibility [J].

Cavasotto, Claudio N. ;

Singh, Narender .

CURRENT COMPUTER-AIDED DRUG DESIGN, 2008, 4 (03) :221-234

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