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Ekins, Sean; Boulanger, Bruno; Swaan, Peter W.; Hupcey, Maggie A. Z.

doi:10.1023/a:1020816005910

Cited by 91 publications

(17 citation statements)

References 90 publications

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“…If more molecules are successful early, they bolster the pipeline and impact The molecular structures of these tyrosinase inhibitors is given in Table 11 J the size of the company portfolio. Computational tools to aid us in this decision making process would ultimately make drug discovery more efficient [76].…”

Section: Discussionmentioning

confidence: 99%

“…Here we have developed some useful tools for the discovery of novel tyrosinase inhibitors from large databases of chemical structures (virtual or in silico). This strategy will "deliver substantial benefits and act as the bedrock for NCE selection" [76] of novel tyrosinase inhibitors which may be used to prevent or treat pigmentation disorders.…”

Section: Discussionmentioning

confidence: 99%

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Atom‐Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results of In Silico Studies Supported by Experimental Results

et al. 2007

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Herein we present results of QSAR studies of tyrosinase inhibitors employing one of the atom-based TOMOCOMD-CARDD (acronym of TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) descriptors, molecular quadratic indices, and Linear Discriminant Analysis (LDA) as pattern recognition method. In this way, a database of 246 organic chemicals, reported as tyrosinase inhibitors having great structural variability, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. In total, 12 LDA-based QSAR models were obtained, the first six with the non-stochastic total and local quadratic indices and the six remaining models with the stochastic molecular descriptors. The best two models for the non-stochastic and stochastic molecular descriptors, showed an appropriate overall accuracy (92.68 and 89.10%, respectively) and a high Matthews correlation coefficient (C of 0.85 and of 0.84, correspondingly) when applied to the training set. External validation series were also used to validate the obtained models; the 91.67% (C ¼ 0.82) and 90.00% (C ¼ 0.78), were correctly classified, respectively. In order to show the possibilities of the present approach for the ligand-based virtual screening of tyrosinase inhibitors, the developed models were used afterwards in a simulation of a virtual search for tyrosinase inhibitors. For instance, more than 93% (93.33%) and 96% (96.66%) of the screened chemicals were correctly classified by the two best LDA-based QSAR models developed with non-stochastic and stochastic quadratic indices, respectively. Finally, the combination of the obtained models permitted the selection/identification of new diterpenoidal alkaloid leads as tyrosinase inhibitors. The found activity is

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Atom‐Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results of In Silico Studies Supported by Experimental Results

et al. 2007

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“…Lead compounds need to satisfy various properties such as ADME [12] (absorption, distribution, metabolism and excretion), toxicity, aqueous solubility, and synthetic accessibility. General de novo design methods deal with only one property: the biological activity for the target.…”

Section: Introductionmentioning

confidence: 99%

Development of a New De Novo Design Algorithm for Exploring Chemical Space

Mishima

Kaneko

Funatsu

2014

Molecular Informatics

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In the first stage of development of new drugs, various lead compounds with high activity are required. To design such compounds, we focus on chemical space defined by structural descriptors. New compounds close to areas where highly active compounds exist will show the same degree of activity. We have developed a new de novo design system to search a target area in chemical space. First, highly active compounds are manually selected as initial seeds. Then, the seeds are entered into our system, and structures slightly different from the seeds are generated and pooled. Next, seeds are selected from the new structure pool based on the distance from target coordinates on the map. To test the algorithm, we used two datasets of ligand binding affinity and showed that the proposed generator could produce diverse virtual compounds that had high activity in docking simulations.

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“…Ultimately, only leads with preferable predicted properties should be synthesized. There have been numerous original papers and reviews that have dealt with various aspects of these early in silico ADME/Tox models that, in most cases, used small numbers of molecules, raising the question of their general applicability [1,2]. Recently, there have also been some timely discussions of the appropriate validation techniques that should be used for quantitative structureactivity relationships (QSARs), although these have perhaps Key words: cytochrome P450, human ether-a-gogo, in silico, quantitative structure-activity relationship (QSAR).…”

Section: Introductionmentioning

confidence: 99%

In silico approaches to predicting drug metabolism, toxicology and beyond

Ekins

2003

Biochemical Society Transactions

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The discovery and optimization of new drug candidates is becoming increasingly reliant upon the combination of experimental and computational approaches related to drug metabolism, toxicology and general biopharmaceutical properties. With the considerable output of high-throughput assays for cytochrome-P450-mediated drug-drug interactions, metabolic stability and assays for toxicology, we have orders of magnitude more data that will facilitate model building. A recursive partitioning model for human liver microsomal metabolic stability based on over 800 structurally diverse molecules was used to predict molecules with known log in vitro clearance data (Spearman's rho -0.64, P <0.0001). In addition, with solely published data, a quantitative structure-activity relationship for 66 inhibitors of the potassium channel human ether-a-gogo (hERG) that has been implicated in the failure of a number of recent drugs has been generated. This model has been validated with further published data for 25 molecules (Spearman's rho 0.83, P <0.0001). If continued value is to be realized from these types of computational models, there needs to be some applied research on their validation and optimization with new data. Some relatively simple approaches may have value when it comes to combining data from multiple models in order to improve and focus drug discovery on the molecules most likely to succeed.

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Cited by 91 publications

References 90 publications

Atom‐Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results of In Silico Studies Supported by Experimental Results

Atom‐Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results of In Silico Studies Supported by Experimental Results

Development of a New De Novo Design Algorithm for Exploring Chemical Space

In silico approaches to predicting drug metabolism, toxicology and beyond

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