3D Pharmacophore Modeling Techniques in Computer‐Aided Molecular Design Using LigandScout

Seidel, Thomas; Bryant, Sharon D.; Ibis, Gökhan; Poli, Giulio; Langer, Thomas

doi:10.1002/9781119161110.ch20

Cited by 36 publications

(24 citation statements)

References 40 publications

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“…When a drug molecule interacts with a target macromolecule, it produces a geometrically and energetically matched active conformation with the target. Medicinal chemists found that different chemical groups in drug molecules have different effects on activity, and changes to some groups have a great influence on the interaction between drugs and targets, while others have little effect [55]. Moreover, It was found that molecules with the same activity tend to have some of the same characteristics.…”

Section: Pharmacophore Modelingmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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Section: Pharmacophore Modelingmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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“…An ideal curve would increase along the Y-axis until it reaches 1, which is the maximum true positive rate, then continues horizontally to the right shown that the hit list contains only the active molecules in the dataset. Two parameters of ROC curve were calculated at different fraction of the model-ordered database (1,5;10 and 100%) including the area under the curve (AUC), which shows the capability of the model to distinguish between true-active and decoy molecules as well as the enrichment factor (EF) which represents the number of true-active compounds found by using the generated pharmacophore mode [ 62 ]. The calculated ROC parameters for the generated model were as follows: AUC 1,5,10,100% 1.0, 1.0, 1.0, and 0.83, respectively; and EF 1;5;10;100%, 57.9; 15.8; 15.8; and 15.8, respectively ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Discovery of anti-MERS-CoV small covalent inhibitors through pharmacophore modeling, covalent docking and molecular dynamics simulation

Alamri

Qamar

Afzal

et al. 2021

Journal of Molecular Liquids

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Middle east respiratory syndrome coronavirus (MERS-CoV) is a fatal pathogen that poses a serious health risk worldwide and especially in the middle east countries. Targeting the MERS-CoV 3-chymotrypsin-like cysteine protease (3CL pro ) with small covalent inhibitors is a significant approach to inhibit replication of the virus. The present work includes generating a pharmacophore model based on the X-ray crystal structures of MERS-CoV 3CL pro in complex with two covalently bound inhibitors. In silico screening of covalent chemical database having 31,642 compounds led to the identification of 378 compounds that fulfils the pharmacophore queries. Lipinski rules of five were then applied to select only compounds with the best physiochemical properties for orally bioavailable drugs. 260 compounds were obtained and subjected to covalent docking-based virtual screening to determine their binding energy scores. The top three candidate compounds, which were shown to adapt similar binding modes as the reported covalent ligands were selected. The mechanism and stability of binding of these compounds were confirmed by 100 ns molecular dynamic simulation followed by MM/PBSA binding free energy calculation. The identified compounds can facilitate the rational design of novel covalent inhibitors of MERS-CoV 3CL pro enzyme as anti-MERS CoV drugs.

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“…The pharmacophore modelling was carried out using LigandScout 4.2 software ( Wolber and Langer, 2005 ). Initially, multiple acceptable conformations for each compound present in the training set were generated using OMEGA conformation model generation method ( Seidel et al., 2017 ; Wolber and Langer, 2005 ) followed by clustering the compounds according to their 3D pharmacophore characteristics. Finally, an independent pharmacophore model for each cluster was created.…”

Section: Methodsmentioning

confidence: 99%

Docking based screening and molecular dynamics simulations to identify potential selective PDE4B inhibitor

Al-Nema

Gaurav

Lee

2020

Heliyon

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Inhibition of phosphodiesterase 4 (PDE4) is a promising therapeutic approach for the treatment of inflammatory pulmonary disorders, i.e. asthma and chronic obstructive pulmonary disease. However, the treatment with non-selective PDE4 inhibitors is associated with side effects such as nausea and vomiting. Among the subtypes of PDE4 inhibited by these inhibitors, PDE4B is expressed in immune, inflammatory and airway smooth muscle cells, whereas, PDE4D is expressed in the area postrema and nucleus of the solitary tract. Thus, PDE4D inhibition is responsible for the emetic response. In this regard, a selective PDE4B inhibitor is expected to be a potential drug candidate for the treatment of inflammatory pulmonary disorders. Therefore, a shared feature pharmacophore model was developed and used as a query for the virtual screening of Maybridge and SPECS databases. A number of filters were applied to ensure only compounds with drug-like properties were selected. Accordingly, nine compounds have been identified as final hits, where HTS04529 showed the highest affinity and selectivity for PDE4B over PDE4D in molecular docking. The docked complexes of HTS04529 with PDE4B and PDE4D were subjected to molecular dynamics simulations for 100ns to assess their binding stability. The results showed that HTS04529 was bound tightly to PDE4B and formed a more stable complex with it than with PDE4D.

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3D Pharmacophore Modeling Techniques in Computer‐Aided Molecular Design Using LigandScout

Cited by 36 publications

References 40 publications

A Review on Applications of Computational Methods in Drug Screening and Design

A Review on Applications of Computational Methods in Drug Screening and Design

Discovery of anti-MERS-CoV small covalent inhibitors through pharmacophore modeling, covalent docking and molecular dynamics simulation

Docking based screening and molecular dynamics simulations to identify potential selective PDE4B inhibitor

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