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We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.

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Pharmacophore‐based techniques are nowadays an important part of many computer‐aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore‐based methods which get mainly applied on synthetic drug‐like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.

Dengue infection is caused by a mosquito-borne virus, particularly in children, which may even cause death. No effective prevention or therapeutic agents to cure this disease are available up to now. The dengue viral envelope (E) protein was discovered to be a promising target for inhibition in several steps of viral infection. Structure-based virtual screening has become an important technique to identify first hits in a drug screening process, as it is possible to reduce the number of compounds to be assayed, allowing to save resources. In the present study, pharmacophore models were generated using the common hits approach (CHA), starting from trajectories obtained from molecular dynamics (MD) simulations of the E protein complexed with the active inhibitor, flavanone (FN5Y). Subsequently, compounds presented in various drug databases were screened using the LigandScout 4.2 program. The obtained hits were analyzed in more detail by molecular docking, followed by extensive MD simulations of the complexes. The highest-ranked compound from this procedure was then synthesized and tested on its inhibitory efficiency by experimental assays.

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Melanogenesis controls the formation of melanin pigment whose overproduction is related to various hyperpigmentary disorders in humans. Tyrosinase is a type‐3 copper enzyme involved in the rate limiting step of melanin synthesis, therefore its inhibition could represent an efficient way for the development of depigmenting agents. In this work, a combination of pharmacophore and docking‐based studies has been employed to screen two in‐house 3D compound databases containing about 2,000 molecules from natural and synthetic sources. As result we selected two “hit compounds” which proved to inhibit tyrosinase activity showing IC50 values in the micromolar range.

In the absence of experimentally derived, three-dimensional structures of receptors in complex with active ligands, it is of high value to be able to gain knowledge about energetically favorable interaction sites solely from the structure of the receptor binding site. For de novo ligand design as well as for lead optimization, this information retrieved from the protein is inevitable. The herein presented method called GRAIL combines the advantages of traditional grid-based approaches for the identification of interaction sites and the power of the pharmacophore concept. A reduced pharmacophoric abstraction of the target system enables the computation of all relevant interaction grid maps in short amounts of time. This allows one to extend the utility of a grid-based method for the analysis of large amounts of coordinate sets obtained by long-time MD simulations. In this way it is possible to assess conformation dependent characteristics of key interactions over time. Furthermore, conformational changes of the protein can be taken into account easily and information thus obtained well-guides a rational ligand design process. A study employing MD trajectories of the oncology target heat shock protein 90 showcases how well our novel approach GRAIL performs for a set of different inhibitors bound to their target protein and how molecular features of the inhibitors are subject to optimization.