Pharmacophore Models for Virtual Screening

Markt, Patrick; Schuster, Daniela; Langer, Thomas

doi:10.1002/9783527633326.ch5

Cited by 8 publications

(6 citation statements)

References 129 publications

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“…Pharmacophore fit score (also referred to as fitness score) was calculated in terms of how well the features in a particular model correctly match with the features of a given chemical compound and, hence, they were used to rank hit molecules retrieved from each model. The fit score was used to calculate the number of features successfully matched and the root-mean-square deviation (RMSD) between a pharmacophore model and the pharmacophoric points of the conformer for a query compound, which in turn is based on inter-feature distance sets (Euclidean function) compared in a pairwise manner (Markt et al, 2011). Additionally, we included exclusion volume spheres (XVols) in each model to represent areas already occupied by the equivalent protein receptor and, thus, inaccessible by the cognate ligand.…”

Section: Methodology Pharmacophore Models Generationmentioning

confidence: 99%

“…Receiver Operating Characteristic (ROC) curves were then constructed by plotting the fraction of true positives out of the total actual positives (i.e., the True Positive Rate or TPR) vs. the fraction of false positives out of the total actual negatives as described in (Rizzi and Fioni, 2008). Finally, to validate each model, we calculated the Area Under Curve (AUC) from each ROC curve at the first (top) 1, 5, 10, and 100% of the database, which was composed of corresponding actives and decoys ranked in terms of their pharmacophore fit scores (Markt et al, 2011).…”

Section: Methodology Pharmacophore Models Generationmentioning

confidence: 99%

“…This is especially critical for pharmacophore models modeling methods in which chemical feature constraints rely on chemical functionalities (mainly supporting universality) instead of molecular graph descriptors, (primarily supporting specificity) (Supplementary Table 3) (Wolber and Langer, 2005;Langer and Wolber, 2006). The retrieval of molecules structurally different to well-known active compounds-and to each other-has previously been discussed as a crucial objective to be achieved in a VS (Grabowski et al, 2008;Stocks et al, 2009;Markt et al, 2011), especially because obtaining different starting scaffolds supposes a larger developability advantage for the identified hit compounds (Scior et al, 2012). However, it might be possible that many chemical entities closely structurally related to their original NP are included within the set of non-extensive fragments and, consequently, partially facilitate the screening of NPDFs that are not necessarily small and poorly structurally diverse.…”

Section: Pharmacophore-based Virtual Screening Could Maintain the Molecular Diversity Of Compoundsmentioning

confidence: 99%

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Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

et al. 2021

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Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

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Section: Methodology Pharmacophore Models Generationmentioning

confidence: 99%

Section: Methodology Pharmacophore Models Generationmentioning

confidence: 99%

Section: Pharmacophore-based Virtual Screening Could Maintain the Molecular Diversity Of Compoundsmentioning

confidence: 99%

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Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

et al. 2021

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“…Pharmacophore is defined as an ensemble of steric and electronic features that is necessary to reach the optimal interactions of a ligand with the catalytic site of a protein and very well accepted in the medicinal chemistry laboratory [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

In Silico Exploration of Novel Tubulin Inhibitors: A Combination of Docking and Molecular Dynamics Simulations, Pharmacophore Modeling, and Virtual Screening

Hadizadeh

Ghodsi

Mirzaei

et al. 2022

Computational and Mathematical Methods in Medicine

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Microtubules play a critical role in mitosis and cell division and are regarded as an excellent target for anticancer therapy. Although microtubule-targeting agents have been widely used in the clinical treatment of different human cancers, their clinical application in cancer therapy is limited by both intrinsic and acquired drug resistance and adverse toxicities. In a previous work, we synthesized compound 9IV-c, ((E)-2-(3,4-dimethoxystyryl)-6,7,8-trimethoxy-N-(3,4,5-trimethoxyphenyl)quinoline-4-amine) that showed potent activity against multiple human tumor cell lines, by targeting spindle formation and/or the microtubule network. Accordingly, in this study, to identify potent tubulin inhibitors, at first, molecular docking and molecular dynamics studies of compound 9IV-c were performed into the colchicine binding site of tubulin; then, a pharmacophore model of the 9IV-c-tubulin complex was generated. The pharmacophore model was then validated by Güner–Henry (GH) scoring methods and receiver operating characteristic (ROC) analysis. The IBScreen database was searched by using this pharmacophore model as a screening query. Finally, five retrieved compounds were selected for molecular docking studies. These efforts identified two compounds (b and c) as potent tubulin inhibitors. Investigation of pharmacokinetic properties of these compounds (b and c) and compound 9IV-c displayed that ligand b has better drug characteristics compared to the other two ligands.

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“…A pharmacophore model is a group of steric and electronic features, which are essential to reach the optimal interactions of a ligand with a protein active site. Today, pharmacophore screening is a well-known method that has two major advantages: first, it significantly increases the speed of the compounds filtering process and second, it allows retrieval of ligands with diverse structures ( 20 ).…”

Section: Introductionmentioning

confidence: 99%

Discovery of direct inhibitor of KRAS oncogenic protein by natural products: a combination of pharmacophore search, molecular docking, and molecular dynamic studies

et al. 2020

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Background and purpose: Aberrant signaling by oncogenic RAS proteins occurs in almost all human tumors. One of the promising strategies to overcome such cancers is the inhibition of KRAS protein, a subtype of RAS family involved in cell growth, differentiation, and apoptosis, through preventing its effector, SOS1, from being attached to the protein. Experimntal approach: Herein, a virtual screening process was performed using pharmacophore search, molecular docking, and molecular dynamic simulations. A pharmacophore model was created to indicate essential features for a KRAS inhibitor and used for screening the National Cancer Institution (NCI) database to retrieve similar compounds to the pharmacophore model with more than 70% similarity. Chosen compounds were then docked into KRAS and four compounds were selected based on the highest binding scores. Next, a similarity search was done in the whole PubChem database to increase the number of potential inhibitors. The filtered compounds were docked again into KRAS and three of them were selected for molecular dynamic simulation. Findings / Results: Compounds 1a , 2d , and 3a can inhibit SOS-iKRAS G12D interaction due to the higher number of interactions with the protein. Moreover, they achieved the equilibrium faster than the approved inhibitor. Conclusion and implications: Auriculasin, a polyphenol flavonoid, can be considered as a potential inhibitor of SOS1-KRAS interaction. This compound seems to be a stronger anticancer than 9LI, a known inhibitor of KRAS, due to its better docking scores. Moreover, this compound can be an appropriate candidate to be formulated as an oral drug.

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Pharmacophore Models for Virtual Screening

Cited by 8 publications

References 129 publications

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

In Silico Exploration of Novel Tubulin Inhibitors: A Combination of Docking and Molecular Dynamics Simulations, Pharmacophore Modeling, and Virtual Screening

Discovery of direct inhibitor of KRAS oncogenic protein by natural products: a combination of pharmacophore search, molecular docking, and molecular dynamic studies

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