pharmacophore models
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Author(s):  
Teng Woei Shy ◽  
Anand Gaurav

Aim: The aim of the present study was to apply pharmacophore based virtual screening to a natural product database to identify potential PDE1B inhibitor lead compounds for neurodegenerative and neuropsychiatric disorders. Background: Neurodegenerative and neuropsychiatric disorders are a major health burden globally. The existing therapies do not provide optimal relief and are associated with substantial adverse effects. This has resulted in a huge unmet medical need for newer and more effective therapies for these disorders. Phosphodiesterase (PDEs) enzymes have been identified as potential targets of drugs for neurodegenerative and neuropsychiatric disorders, and one of the subtypes, i.e., PDE1B, accounts for more than 90 % of total brain PDE activity associated with learning and memory process, making it an interesting drug target for the treatment of neurodegenerative disorders. Objectives: The present study has been conducted to identify potential PDE1B inhibitor lead compounds from the natural product database. Methods: Ligand-based pharmacophore models were generated and validated; they were then employed for virtual screening of Universal Natural Products Database (UNPD) followed by docking with PDE1B to identify the best hit compound. Results: Virtual screening led to the identification of 85 compounds which were then docked into the active site of PDE1B. Out of the 85 compounds, six showed a higher affinity for PDE1B than the standard PDE1B inhibitors. The top scoring compound was identified as Cedreprenone. Conclusion: Virtual screening of UNPD using Ligand based pharmacophore led to the identification of Cedreprenone, a potential new natural PDE1B inhibitor lead compound.


Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7201
Author(s):  
Christian Permann ◽  
Thomas Seidel ◽  
Thierry Langer

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.


2021 ◽  
pp. 105480
Author(s):  
Martina Pierri ◽  
Erica Gazzillo ◽  
Maria Giovanna Chini ◽  
Maria Grazia Ferraro ◽  
Marialuisa Piccolo ◽  
...  

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Stefan M. Kohlbacher ◽  
Thierry Langer ◽  
Thomas Seidel

AbstractQSAR methods are widely applied in the drug discovery process, both in the hit‐to‐lead and lead optimization phase, as well as in the drug-approval process. Most QSAR algorithms are limited to using molecules as input and disregard pharmacophores or pharmacophoric features entirely. However, due to the high level of abstraction, pharmacophore representations provide some advantageous properties for building quantitative SAR models. The abstract depiction of molecular interactions avoids a bias towards overrepresented functional groups in small datasets. Furthermore, a well‐crafted quantitative pharmacophore model can generalise to underrepresented or even missing molecular features in the training set by using pharmacophoric interaction patterns only. This paper presents a novel method to construct quantitative pharmacophore models and demonstrates its applicability and robustness on more than 250 diverse datasets. fivefold cross-validation on these datasets with default settings yielded an average RMSE of 0.62, with an average standard deviation of 0.18. Additional cross-validation studies on datasets with 15–20 training samples showed that robust quantitative pharmacophore models could be obtained. These low requirements for dataset sizes render quantitative pharmacophores a viable go-tomethod for medicinal chemists, especially in the lead-optimisation stage of drug discovery projects.


2021 ◽  
Author(s):  
Shazia Haider ◽  
Zafar Saify ◽  
Nousheen Mushtaq ◽  
Faheema Siddiqui ◽  
Toqeer Rao ◽  
...  

Abstract In the study of designing pharmacophore models for analgesic, a series of 4-[4–chloro-3- (trifluoromethyl)-phenyl]-4-piperidinol (TFMP) derivatives were synthesized and characterized by physical and spectral method (HR-EIMS, HR-FABMS, 1H-NMR, 13C-NMR, UV, and FT-IR). The analgesic action of the synthesized derivatives was estimated by means of Hot Plate Method. Most of the compounds displayed potent analgesic efficacy and an ultrashort to long duration of action. The results indicate that these compounds are useful as analgesics. Qualitatively nine compounds resemble morphine in pharmacological action, whereas three derivatives are devoid of any significant analgesic potential. In conclusion, among active compounds 3 (188%), 5 (137%), 6 (162%), and 8 (107%) respectively emerged as most effective analgesic and they depressed peripheral and centrally mediated pain by opioid dependent and independent systems.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad M. Al-Sanea ◽  
Garri Chilingaryan ◽  
Narek Abelyan ◽  
Grigor Arakelov ◽  
Harutyun Sahakyan ◽  
...  

AbstractHuman carbonic anhydrase XII (hCA XII) isozyme is of high therapeutic value as a pharmacological target and biomarker for different types of cancer. The hCA XII is one of the crucial effectors that regulates extracellular and intracellular pH and affects cancer cell proliferation, invasion, growth and metastasis. Despite the fact that interaction features of hCAs inhibitors with the catalytic site of the enzyme are well described, lack in the selectivity of the traditional hCA inhibitors based on the sulfonamide group or related motifs is an urgent issue. Moreover, drugs containing sulfanomides can cause sulfa allergies. Thus, identification of novel non-classical inhibitors of hCA XII is of high priority and is currently the subject of a vast field of study. This study was devoted to the identification of novel potential hCA XII inhibitors using comprehensive set of computational approaches for drug design discovery: generation and validation of structure- and ligand-based pharmacophore models, molecular docking, re-scoring of virtual screening results with MMGBSA, molecular dynamics simulations, etc. As the results of the study several compounds with alternative to classical inhibitors chemical scaffolds, in particular one of coumarins derivative, have been identified and are of high interest as potential non-classical hCA XII inhibitors.


Author(s):  
Divya Sharma ◽  
Salahuddin ◽  
Vikas Sharma ◽  
Rajnish Kumar ◽  
Sagar Joshi ◽  
...  

: Cancer is a kind of disease that has scared many people for many years. Cancer is due to the excessive growth of cells in every particular part of the body. Oxadiazole 1,3,4 is a magical organic moiety that has anticancer potential. Various works on the 1,3,4-oxadiazoles moiety showing anticancer activity have been reported. The present analysis summarizes general synthetic methods for 1,3,4 oxadiazole. Different receptors on which these drug acts are discussed. Pharmacophore models are also presented in this review for topoisomerase-I, histone deacetylase, epidermal growth factor enzymes.


Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3115
Author(s):  
Minh-Tri Le ◽  
Viet-Nham Hoang ◽  
Dac-Nhan Nguyen ◽  
Thi-Hoang-Linh Bui ◽  
Thien-Vy Phan ◽  
...  

ABCG2 is an ABC membrane protein reverse transport pump, which removes toxic substances such as medicines out of cells. As a result, drug bioavailability is an unexpected change and negatively influences the ADMET (absorption, distribution, metabolism, excretion, and toxicity), leading to multi-drug resistance (MDR). Currently, in spite of promising studies, screening for ABCG2 inhibitors showed modest results. The aim of this study was to search for small molecules that could inhibit the ABCG2 pump. We first used the WISS MODEL automatic server to build up ABCG2 homology protein from 655 amino acids. Pharmacophore models, which were con-structed based on strong ABCG2 inhibitors (IC50 < 1 μM), consist of two hydrophobic (Hyd) groups, two hydrogen bonding acceptors (Acc2), and an aromatic or conjugated ring (Aro|PiR). Using molecular docking method, 714 substances from the DrugBank and 837 substances from the TCM with potential to inhibit the ABCG2 were obtained. These chemicals maybe favor synthesized or extracted and bioactivity testing.


2021 ◽  
Author(s):  
Stefan M. Kohlbacher ◽  
Thierry Langer ◽  
Thomas Seidel

Abstract QSAR methods are widely applied in the drug discovery process, both in the hit‑to‑lead and lead optimization phase, as well as in the drug-approval process. Most QSAR algorithms are limited to using molecules as input and disregard pharmacophores or pharmacophoric features entirely. However, due to the high level of abstraction, pharmacophore representations provide some advantageous properties for building quantitative SAR models. The abstract depiction of molecular interactions avoids a bias towards overrepresented functional groups in small datasets. Furthermore, a well‑crafted quantitative pharmacophore model can generalise to underrepresented or even missing molecular features in the training set by using pharmacophoric interaction patterns only. This paper presents a novel method to construct quantitative pharmacophore models and demonstrates its applicability and robustness on more than 250 diverse datasets. 5‑fold cross-validation on these datasets with default settings yielded an average RMSE of 0.62, with an average standard deviation of 0.18. Additional cross-validation studies on datasets with 15-20 training samples showed that robust quantitative pharmacophore models could be obtained. These low requirements for dataset sizes renders quantitative pharmacophores a viable go-to method for medicinal chemists, especially in the lead-optimisation stage of drug discovery projects.


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