BUILDER v.2: Improving the chemistry of a de novo design strategy

Roe, Diana C.; Kuntz, Irwin D.

doi:10.1007/bf00124457

Cited by 81 publications

(28 citation statements)

References 47 publications

(44 reference statements)

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“…Docking programs e.g. CombiDock [142], DREAM++ [143], BUILDER v.2 [144], OptiDock [145], CombiGlide (Schrödinger), and CombiSMoG [146] have been specifically developed for structure-based library design. Here, we highlight a small set of applications for structurebased library design (Table 3).…”

Section: Structure-based Ligand Library Designmentioning

confidence: 99%

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

Chen¹,

Morrow²,

Tran³

et al. 2012

curr drug metab

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As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. KeywordsStructure-based drug design; protein modeling; focused library design; pharmacophore; flexible docking; high-throughput virtual screening; de novo design; protein-protein interaction; polypharmacology Modern computational-aided drug design established a novel platform by which researchers perform in-depth in silico simulation prior to labor-extensive wet-lab validation [1]. It comprises of two major parts corresponding to the information of molecular source it utilizes: structure-based (or receptor-based) drug design and ligand-based drug design. Structure-based drug design, which relies on the knowledge of biological target structures, aims to discover small molecules/peptides leads with desired chemistry properties, and orchestrate the following experimental validation and lead optimization. Structure-based approach provides mechanism-based basis, where potential ligands are excavated using receptor-dependent parameters, while ligand-based approaches bypass the consideration of complex biomolecular "black box" in a living cell. This in silico method permeates all aspects of drug discovery today [2], and we expect it will draw more attentions with the unprecedented advances of computational power and modeling accuracy in this decade.* Corresponding author: Shuxing Zhang, Ph.D., The University of Texas M. D. Anderson Cancer Center, Department of Experimental Therapeutics, Unit 1950, 1901 East Rd., Houston, TX 77054, Telephone: (713)-745-2958 794-5577, shuzhang@mdanderson.org. Conflict of interestThe authors declare that they do not have competing interests. NIH Public Access Author ManuscriptCurr Pharm Des. Author manuscript; available in PMC 2013 November 07.Published in final edited form as:Curr Pharm Des. 2012 ; 18(9): 1217-1239. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptIn this review, we will overview the state-of-the-art structure-based drug discovery techniques ranging from receptor modeling to lead identification and optimization. In each topic, we will also highlight the fruitful successes as well as ch...

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Section: Structure-based Ligand Library Designmentioning

confidence: 99%

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

Chen¹,

Morrow²,

Tran³

et al. 2012

curr drug metab

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“…GRID (Goodford, 1985) is the first de novo method. Then, de novo ligand design programs such as LEGEND (Nishibta and Itai, 1993), LeapFrog (http:// www.tripos.com), LigBuilder (Wang et al, 2000), SPROUT (http://www.simbiosys.ca/sprout/index.html), CONCERTS (Pearlman and Murcko, 1996), PRO-LIGAND (Waszkowycz et al, 1994), TOPAS (Schneider et al, 2000), BUILDER (Roe and Kuntz, 1995), and LUDI (http://www.accelrys.com, Böhm, 1992a,b) were developed to build up structures according to the active site of the receptor. De novo design generates functional groups in suitable forms and suitable conformations to interact with the protein surface.…”

Section: De Novo Ligand Designmentioning

confidence: 99%

Computer‐Aided Drug Design

Chen

Chern

2006

Drug Discovery Research

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“…If the structure of the receptor is known, one can use software packages like LUDI [59][60][61] and BUILDER [140], which combine identification of receptor-based pharmacophore with de novo design. Other examples of de novo design algorithms can be found in [122,141].…”

Section: Use Of Pharmacophores In De Novo Design Of Ligandsmentioning

confidence: 99%

Predicting Molecular Interactions in silico: I. A Guide to Pharmacophore Identification and its Applications to Drug Design

Dror

Shulman‐Peleg²,

Nussinov³

et al. 2004

CMC

144

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A major goal in contemporary drug design is to develop new ligands with high affinity of binding toward a given protein receptor. Pharmacophore, which is the three-dimensional arrangement of essential features that enable a molecule to exert a particular biological effect, is a very useful model for achieving this goal. If the three-dimensional structure of the receptor is known, pharmacophore is a complementary tool to standard techniques, such as docking. However, frequently the structure of the receptor protein is unknown and only a set of ligands together with their measured binding affinities towards the receptor is available. In such a case, a pharmacophore-based strategy is one of the few applicable tools.Here we present a broad, yet concise guide to pharmacophore identification and review a sample of applications for drug design. In particular, we present the framework of the algorithms, classify their modules and point out their advantages and challenges.

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BUILDER v.2: Improving the chemistry of a de novo design strategy

Cited by 81 publications

References 47 publications

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

Computer‐Aided Drug Design

Predicting Molecular Interactions in silico: I. A Guide to Pharmacophore Identification and its Applications to Drug Design

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