Pharmacophore Hypotheses and Molecular Comparisons

Drug Design ◽  
2013 ◽  
pp. 349-370
Author(s):  
Gerhard Klebe
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Mutasem O. Taha

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. TTK targeting received recent concern for the enhancement of possible anticancer therapies. Objective: In this regard we employed our well-known method of QSAR-guided selection of best crystallographic pharmacophore(s) to discover considerable binding interactions that anchore inhibitors into TTK1 binding site. Method:Sixtyone TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that when combined with other physicochemical descriptors elucidates bioactivity discrepancy within a list of 55 miscellaneous inhibitors. Results: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. Conclusion: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 micM.


PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e84510 ◽  
Author(s):  
Dawid Warszycki ◽  
Stefan Mordalski ◽  
Kurt Kristiansen ◽  
Rafał Kafel ◽  
Ingebrigt Sylte ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Shikhar Gupta ◽  
C. Gopi Mohan

In this study, we have employedin silicomethodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer’s randomization technique. The best acetylcholinesterase and butyrylcholinesterase inhibitors pharmacophore hypotheses Hypo1_A and Hypo1_B, with high correlation coefficient of 0.96 and 0.94, respectively, were used as 3D query for screening the Zinc database. The screened hits were then subjected to the ADME/T and molecular docking study to prioritise the compounds. Finally, 18 compounds were identified as potential leads against AChE enzyme, showing good predicted activities and promising ADME/T properties.


2004 ◽  
Vol 18 (11) ◽  
pp. 665-682 ◽  
Author(s):  
Simon J. Cottrell ◽  
Valerie J. Gillet ◽  
Robin Taylor ◽  
David J. Wilton

2021 ◽  
Author(s):  
Mariia Matveieva ◽  
Pavel Polishchuk

Abstract Interpretation of QSAR models is useful to understand the complex nature of biological or physicochemical processes, guide structural optimization or perform knowledge-based validation of QSAR models. Highly predictive models are usually complex and their interpretation is non-trivial. This is particularly true for modern neural networks. Various approaches to interpretation of these models exist. However, it is difficult to evaluate and compare performance and applicability of these ever-emerging methods. Herein, we developed several benchmark data sets with end-points determined by pre-defined patterns. These data sets are purposed for evaluation of the ability of interpretation approaches to retrieve these patterns. They represent tasks with different complexity levels: from simple atom-based additive properties to pharmacophore hypotheses. We proposed several quantitative metrics of interpretation performance. Applicability of benchmarks and metrics was demonstrated on a set of conventional models and end-to-end graph convolutional neural networks interpreted by the previously suggested universal ML-agnostic approach for structural interpretation. We anticipate these benchmarks to be useful in evaluation of new interpretation approaches and investigation of decision making of complex “black box” models.


ChemMedChem ◽  
2011 ◽  
Vol 6 (9) ◽  
pp. 1533-1533 ◽  
Author(s):  
Birte Seebeck ◽  
Markus Wagener ◽  
Matthias Rarey

ChemInform ◽  
2005 ◽  
Vol 36 (22) ◽  
Author(s):  
Rajendra Kristam ◽  
Valerie J. Gillet ◽  
Richard A. Lewis ◽  
David Thorner

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