Two- and three-dimensional quantitative structure–activity relationships for a series of purine nucleoside phosphorylase inhibitors

Castilho, Marcelo Santos; Postigo, Matheus P.; Paula, Caroline Busatta Vaz de; Montanari, Carlos Alberto; Oliva, Glaucius; Andricopulo, Adriano D.

doi:10.1016/j.bmc.2005.08.055

Cited by 61 publications

(82 citation statements)

References 25 publications

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“…Analyzing the contribution map of Fig. 3a, it can be observed that the molecular fragments for the purine ring and cyanil moieties are strongly involved in the inhibitory profile of the compound as previously described [20]. Thus, it is possible to identify specific individual atomic contributions as determinants of increased potency in the absence of the 3D crystal structure of PNP or any other 3D structural information.…”

Section: Hqsar: a Versatile Tool In Drug Designmentioning

confidence: 59%

“…Several HQSAR models for a variety of standard data sets consisting of ligands of important molecular targets have been generated throughout recent years [20,21,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40], including cases where the 3D structure of the target protein was not yet available or the molecular target was unknown. It is important to note that besides predicting accurately property values of untested compounds (e.g., potency, affinity), HQSAR models can also provide useful insights into the relationships between structural fragments (micromolecule) and biological activity [25].…”

Section: Hqsar: a Versatile Tool In Drug Designmentioning

confidence: 99%

“…For example, this principle can be applied to differentiate the 2D HQSAR contribution map for the most potent compound (IC 50 = 10 nM) of a large collection of 9-substituted-9-deazaguanine analogs that inhibit human purine nucleoside phosphorylase (PNP) enzyme (Fig. 3a) [20]. The contribution map allowed the identification of key structural components essential for intermolecular interactions in the protein active site.…”

Section: Hqsar: a Versatile Tool In Drug Designmentioning

confidence: 99%

“…However, when 3D structural information is available, this allows a comparison of the HQSAR results to determine if they are in agreement with the 3D chemical environment of the target protein (Fig. 3b) [20].…”

Section: Hqsar: a Versatile Tool In Drug Designmentioning

confidence: 99%

“…QSAR is a valuable drug design tool that applies statistical analysis of relationships between chemical structure and biological activity in a quantitative and mechanism-oriented manner [16][17][18][19]. QSAR technologies has been employed, and continue to be developed and employed, both to correlate information in data sets and to facilitate, for example, the discovery of enzyme inhibitors, agonist or antagonists of important drug targets [20][21][22][23][24]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Fragment-based QSAR: perspectives in drug design

Salum

Andricopulo

2009

Mol Divers

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Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

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Section: Hqsar: a Versatile Tool In Drug Designmentioning

confidence: 59%

Section: Hqsar: a Versatile Tool In Drug Designmentioning

confidence: 99%

Section: Hqsar: a Versatile Tool In Drug Designmentioning

confidence: 99%

Section: Hqsar: a Versatile Tool In Drug Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fragment-based QSAR: perspectives in drug design

Salum

Andricopulo

2009

Mol Divers

Self Cite

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Recent Trends in Structure‐Based Drug Design and Energetics

Andricopulo

Güido

Trivella

et al. 2010

Burger's Medicinal Chemistry and Drug Discovery

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The integration of cheminformatics tools, thermodynamic data, and structural information play a major role in the drug discovery process. Altogether, these methods can describe the molecular forces that govern the affinity and selectivity of bioactive molecules for their macromolecular targets. By being able to uncover the relationships between structure and energetics when using high‐resolution structural information and modern biophysical methods, one can fulfill the challenge of correctly interpreting drug–macromolecular interactions. These interactions are prone to be unveiled and scrutinized when structure‐based drug design is applied to a known three‐dimensional (3D) structure of a given protein. If fully integrated with structure‐based ligand design in an iterative way, computational methods can be of invaluable help to describe the ligand–target (cocomplex) formation. Structure‐based virtual screening can be used to rapid cherry‐pick the best candidates from a large pool of compounds in a chemical library after docking into the active sites of 3D protein structures. To pursue this, the quantification of favorable and unfavorable interactions requires knowledge of the thermodynamics of the interactions. Docking algorithms and molecular dynamics simulations can be used to predict binding energies for positioning ligands in target binding sites, but they only provide information regarded to the prediction of the change in the Gibbs free energy change, which hampers the thoroughly dissection of all other very important thermodynamic parameters—enthalpy, entropy, and heat capacity change. The major goal of this chapter is to show the integration of structure‐based drug design with the energetic. Microcalorimetric measurement of the drug–macromolecular interaction is an effective way to enhance the power, the medicinal chemists have on hand, to pursue a knowledge‐based approach toward the description of all of the noncovalent bond terms that take place in the cocomplex formation.

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Computer Techniques: Identifying Similarities Between Small Molecules

Meek

Moyna

Zauhar

2010

Pharmaceutical Sciences Encyclopedia

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Defining similarity between molecules is a complex task. There is a vast expanse of in silico techniques that “detect” similarity between small molecules. In silico search techniques prove most useful when the similarity measurement (scoring index) reliably returns molecules with the same desired characteristics as the query. This article focuses on current computational methodologies available for the comparison of structurally similar compounds.

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Two- and three-dimensional quantitative structure–activity relationships for a series of purine nucleoside phosphorylase inhibitors

Cited by 61 publications

References 25 publications

Fragment-based QSAR: perspectives in drug design

Fragment-based QSAR: perspectives in drug design

Recent Trends in Structure‐Based Drug Design and Energetics

Computer Techniques: Identifying Similarities Between Small Molecules

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