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Makino, Shingo; Ewing, Todd; Kuntz, Irwin D.

doi:10.1023/a:1008066310669

Cited by 72 publications

(26 citation statements)

References 43 publications

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“…The placement of the core is done in the following ways. It can be derived from docking of a representative sample of products [11], or coordinates of the core can be obtained by docking the core itself [12][13][14]. Finally, the core can be placed on the basis of known interactions of similar compounds [15].…”

Section: Introductionmentioning

confidence: 99%

Surrogate docking: structure-based virtual screening at high throughput speed

Yoon

Smellie

Hartsough

et al. 2005

J Comput Aided Mol Des

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Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing approximately 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size - not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of approximately 13 and approximately 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.

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Section: Introductionmentioning

confidence: 99%

Surrogate docking: structure-based virtual screening at high throughput speed

Yoon

Smellie

Hartsough

et al. 2005

J Comput Aided Mol Des

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“…Unlike other combinatorial docking programs [27][28][29][30][31][32][33][34], the BP structure-based virtual screening method described here does not perform in situ chemical reactions during docking. The enumeration and docking steps are modular and completely independent.…”

Section: (D)mentioning

confidence: 99%

“…Structure-based virtual screening of combinatorial libraries without docking the whole libraries has been explored by several groups before, majority of them by taking advantage of the array nature of the libraries [26][27][28][29][30][31][32][33][34][35][36][37][38]. Examples are CombiDOCK and DREAM??…”

Section: Introductionmentioning

confidence: 99%

“…Examples are CombiDOCK and DREAM?? by Kuntz and coworkers [27,28], the VLSprout algorithm by Johnson and coworkers [29], Core-template-based method by Leach et al [30], OptiDock by Sprout et al [31], pharmacophore-combinatorial docking combination by Gastreich et al [32], FLEXX C by Rarey and Lengauer [33], and FLEXNOVO by Degen and Rarey [33]. Other efficient docking methods, such as LibDock by Diller and Merz [35,36] and TrixX by Schellhammer and Rarey [37], can also be used for structure-based virtual screening of combinatorial libraries.…”

Section: Introductionmentioning

confidence: 99%

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Combinatorial library-based design with Basis Products

Zhou

Shi

et al. 2009

J Comput Aided Mol Des

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Uncovering useful lead compounds from a vast virtual library of synthesizable compounds continues to be of tremendous interest to pharmaceutical researchers. Here we present the concept of Basis Products (BPs), a new and broadly applicable method for achieving efficient selections from a combinatorial library. By definition, Basis Products are a strategically selected subset of compounds from a potentially very large combinatorial library, and any compound in a combinatorial library can represented by its BPs. In this article we will show how to use BP docking scores to find the top compounds of a combinatorial library. Compared with the brute-force docking of an entire virtual library, docking with BPs are much more efficient because of the substantial size reduction, saving both time and resources. We will also demonstrate how BPs can be used for property-based combinatorial library designs. Furthermore, BPs can also be considered as fragments carrying chemistry knowledge, hence they can potentially be used in combination with any fragment-based design method. Therefore, BPs can be used to integrate combinatorial design with structure-based design and/or fragment-based design. Other potential applications of BPs include lead hopping and consensus core building, which we will describe briefly as well in this report.

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“…For the purposes of combinatorial design special combinatorial docking procedures have been developed. DREAM++ [55], CombiDOCK [56], and CombiSMOG [57] use algorithms that combine combinatorial ligand design and fast docking techniques [1].…”

Section: Fragment-based De Novo Designmentioning

confidence: 99%

Library Design

Stoll¹

2003

Chimia

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This review aims at giving a short introduction to the most important areas of library design. The description of compounds by descriptors and fingerprints, and similarity-based clustering techniques are illustrated in the context of untargeted library design. For lead finding and lead optimization libraries it touches on ligand-based combinatorial design, structure-based design, docking and scoring techniques, and fragmentbased de novo design. It is shown that computational and combinatorial chemistry can be successfully combined in the design process.

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Cited by 72 publications

References 43 publications

Surrogate docking: structure-based virtual screening at high throughput speed

Surrogate docking: structure-based virtual screening at high throughput speed

Combinatorial library-based design with Basis Products

Library Design

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