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WIREs Comput Mol Sci
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Next generation 3D pharmacophore modeling

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Abstract 3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond. This article is categorized under: Computer and Information Science > Chemoinformatics Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Interactions
3D pharmacophore generation approaches based on the available data. 3D pharmacophores can be generated from either a set of known ligands, atomistic models of ligand‐macromolecular target complexes or the sole macromolecular (apo) target
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PyRod applied to the binding pocket of cyclin‐dependent kinase 2. (a) The protein environment of water molecules is analyzed to generate (b) dynamic molecular interaction fields (dMIFs) describing the pharmacophoric characteristics of the binding pocket, (c) which can be translated into pharmacophore features for virtual screening. Yellow—hydrophobic contact, green—hydrogen bond donor, red—hydrogen bond acceptor
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Dynophores (dynamic pharmacophores) unveil dynamic binding mode changes of the sphingosine‐1‐phosphate receptor ligand ML056 during a 100 ns MD simulation. The yellow point clouds indicate lipophilic contacts, the red and green features represent hydrogen bond acceptor or donor, respectively, and a positively charged area is shown as a blue point cloud. The percentages next to the features refer to their occurrence frequency during the simulation. In the example shown, a major part of the molecule remains in its initial orientation resulting in nearly sphere‐like distributions of the according feature point clouds (right part). The lipophilic tail is much more flexible within the binding site as indicated by the banana‐shaped feature cloud (left part). MD, molecular dynamics
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Discovery of covalent inhibitors of viral 3C protease. The initial fragment was identified with a 3D pharmacophore and further optimized by scaffold hopping and subsequent fragment growing. Green arrow—hydrogen bond donor, red arrow—hydrogen bond acceptor, yellow sphere—lipophilic contact, orange sphere—residue binding point
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Virtual screening workflow. 3D pharmacophores are generated with either structure‐ or ligand‐based approaches. State‐of‐the art retrospective validation is performed by plotting ROC curves with elaborated sets of actives and decoys. Pharmacophore‐based virtual screening is often followed by computationally more expensive methods such as docking or molecular dynamics simulations to get more differentiated structural insights. ROC, receiver operating characteristics
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Molecular and Statistical Mechanics > Molecular Interactions
Computer and Information Science > Computer Algorithms and Programming
Computer and Information Science > Chemoinformatics

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