Pharmacophore-based compound modeling, virtual screening, and bio-activity profiling has become one of the most popular in silico techniques for supporting medicinal chemists in their hit finding, hit expansion, hit to lead, and lead optimization programs. The molecular design tool LigandScout^[1] has been developed to address successfully one of the most important issues in virtual screening: Enhancing early enrichment while maintaining high computational speed as well as ease of use, as shown by reference studies.^[2]

As an extension of the static pharmacophore approach, we lately have focused on incorporating dynamic effects of ligand protein binding into our automated interaction determination process. The so-called Common Hits Approach (CHA)^[3] uses the multiple coordinate sets saved during MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled, thus reducing the high number to only a few hundred representative pharmacophore models. Virtual screening runs are then performed with every representative pharmacophore model and the screening results are combined and re-scored to generate a single hit list. The score for a particular molecule is then calculated based on the number of representative pharmacophore models, which classified a particular molecule as being active. Finally, the recently developed GRAIL (GRids of phArmacophore Interaction fieLds)^[4]method 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.

[1] Wolber G, Langer T, J Chem Inf Model. 2005; 45(1): 160-69.

[2] Karaboga AS, Planesas JM, Petronin F, Teixido J, Souchet M, Perez-Nueno VI. J Chem Inf Model. 2013; 53(3):1043-56.

[3] Wieder M, Garon A, Perricone U, Boresch S, Seidel T, Almerico AM, Langer T, J Chem Inf Model. 2017; 57(2):365-85. 

[4] Schuetz DA, Seidel T, Garon A, Martini R, Körbel M, Ecker GF, Langer T, J Chem Theory Comput. 2018; 14: 4958-4970.