Pharmacophore-Based VS

LigandScout generates the excluded volumes on the basis of the Ca atoms of amino acids defining the binding pocket. 

In the subsequent pharmacophore search, the screened compounds were required to match at least four out of the five essential pharmacophoric features. The screening resulted in 6232 compounds (3.3%) that passed the LigandScout pharmacophore model. Since it was likely that not all the 6232 hits were PRMTl inhibitors, we further filtered the compound selection by carrying out a docking study. 

Compound ICsoiSE (fiM) hPRMTl Tanimoto coefficient MACCS keys Tanimoto coefficient graph-3-point pharmacophore Actual docking rank COLD 

The Tanimoto similarity indices are calculated on the basis of MACCS keys and graph-3-point pharmacophore fingerprints. The actual docking rank from the docking of the 6236 Chembridge compounds is indicated. 

In general, the VS on PRMTl was successful. Both the selected compounds identified as virtual hits and the randomly selected NCI compounds predicted to be inactive were correctly classified. Of course, the limited number of compounds tested cannot be used to make a final judgment of the performance and accuracy of the applied VS approach. However, from the point of view of a medicinal chemist, it is more important to obtain biologically active compounds or ideas for further analogues out of a VS campaign than producing overfitted models based on retrospecitve studies. 

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Methods that deduce a pharmacophore, an arrangement in 3D space of features that contribute or detract from binding and look for its presence in the database that is searched. This method places emphasis on features like hydrogen bond donors, hydrogen bond acceptors, acidic or basic units and hydrophobic fragments and opens the possibility of identifying unexpected scaffolds with required features (pharmacophore-based VS or PHBVS). 

Both in the pharmaceutical industry and software companies specialized in computer-aided molecular design the demand for experts in the field interfacing medicinal chemistry and computer sciences will increase within the next decade. There is no doubt that we will experience an exciting period of substantial progress in pharmacophore-based VS technologies in the near future. 

Since in pharmacophore-based VS only a part of the database molecules matches the model and therefore obtains an alignment score, a limited number of Se and 1-Sp values can be calculated and plotted. Thus, a selective model would result in an ROC curve that starts at the origin and finishes before it reaches the upper right comer where all active and inactive molecules are scored. However, to simplify the explanation of the ROC curve method, we suggest that the model would retrieve and score all database molecules, either because all database molecules are very similar or because partial matching of model features is allowed. In this case, an ideal pharmacophore model will score all actives higher than inactive database molecules. 

Identification of Nanomolar Histamine H3 Receptor Antagonists by Structure-and Pharmacophore-Based VS... 

Keywords VS, Virtual Screening, Lead discovery, lead, HTS, Pharmacophore-Based, Structure-Based, Fragment-based, Ligand-based, Docking, Scoring, hybrid workflows, VS strategy, Benchmarking VS... 

In this section, a number of case studies (Table 5) in which different types of VS methods are combined into a hybrid workflow. Often these combine a fast, ligand or pharmacophore-based method with a later docking method. The latter is useful at the inspection stage as it allows the molecule to be reviewed within the context of the protein binding site. A poor binding pose can be an indicator of a poor fit. Furthermore, possible interactions outside the scope of the molecules used to train the ligand-based method can be identified. 

This surface area classification notion naturally can be extended to other properties. For example, a collection of pharmacophore-type VS A descriptors can be calculated by summing the V, contribution of each in a molecule of a specific type. For example, if the atom classes are donor, acceptor, polar, hydrophobe, anion, and cation, then six VSA descriptors can be calculated such that for any given molecule the sum of the six descriptors is the VSA of the entire molecule and each descriptor is the VSA of all atoms one of the six classes. Such descriptors can be used for rough pharmacophore-based similarity measures. 

Pharmacophore-based screening is a virtual screening (VS) method used to automatically evaluate millions of compounds by computer programs [16]. A variety of applications for VS exist. It can be applied to obtain novel lead structures from corporate or commercial databases in early drug discovery, to search virtual... 

The use of pharmacophore-based screening in the field of natural product drug discovery is one of the most recent application areas of pharmacophore models. It has been made possible by the introduction of several natural product databases for VS in... 

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