Assessing the Performance of 3D Pharmacophore Models in Virtual Screening: How Good are They?

Abstract

Pharmacophore approaches have evolved to be one of the most successful tools in drug discovery, especially since the past two decades. 3D pharmacophore methods are now commonly used as part of more complex workflows in drug discovery campaigns, and have been successfully and extensively applied in virtual screening (VS) approaches. This review provides a perspective of how to assess the performance of 3D pharmacophore models to be used in VS. Since 3D VS protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of modeling and external sets on the outcome of evaluations. Moreover, we highlight the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D pharmacophore-based virtual screening methods.

Targeting surface voids to counter membrane disorders in lipointoxication-related diseases

“…A database of 80 fatty acid derivatives containing 14 tested active and 66 inactive compounds on the desired target (Deguil et al, 2011) was screened against these pharmacophores, without taking into account the exclusion volumes. The quality of the pharmacophore models was quantitatively evaluated by calculating the selectivity, specificity and accuracy for each model separately (Braga and Andrade, 2013). Virtual screening was performed on the selected pharmacophore using LigandScout on different databases that represent around 6.5 million commercially available compounds coming from the Zinc database Drug-now subset and various commercial suppliers.…”

Application of the methods of molecular modeling to the search for new biologically active substances

“…ROC (receiver operating characteristic) -як інструмент оцінки результатів скринінгу, посідає особливе місце серед бінарних алгоритмів класифікації [23]. У нашому разі за допомогою ROC-кривих визначили здатність фармакофорних моделей класифікувати речови-ни на активні і неактивні, розраховуючи значен-ня AUC (Area Under Curve) [24,25].…”

Benchmarking methods and data sets for ligand enrichment assessment in virtual screening

“…The SBVS approaches, often referred to be molecular docking, employ the three-dimensional target structure to identify molecules that potentially bind to the target with appreciable affinity and specificity [10, 16, 20]. The latter is normally similarity-based, which identifies compounds of novel chemotypes but with similar activities by mining the information of known ligands [5, 11, 12, 17, 21–23]. …”