In this review, we give a comprehensive overview of 3D pharmacophore models, their usage in drug design, and current developments in the field. 3 3D pharmacophores present a unique opportunity to harvest the enormous available chemical space for drug-like molecules. 2 Additionally, current developments in machine learning algorithms allow for in silico generation of billions of theoretically synthesizable molecules. 1 The chemical space for molecules with a molecular weight below 500 Da is estimated to contain at least 10 60 organic molecules.
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Although the concept of 3D pharmacophores was developed at the beginning of the 19th century, virtual screening experiments were not performed until the late 80s and early 90s, when the first software packages for database searches were released. The high degree of abstraction in 3D pharmacophores enables the rationalization of binding modes for chemically diverse ligands and, subsequently, rapid and highly efficient virtual screening of molecular databases. 3D pharmacophores represent an intuitive and powerful description of these interaction patterns. The way in which ligands bind to their macromolecular targets is based on a small set of chemical interactions (chemical features), such as hydrogen bonds, charges, or lipophilic contacts. Macromolecular biological structures such as proteins or DNA bind small organic molecules triggering functional modulation and biological response. Molecular and Statistical Mechanics > Molecular Interactions.Computer and Information Science > Computer Algorithms and Programming.Computer and Information Science > Chemoinformatics.The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond. 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 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. Moreover, we discuss recent developments in the field. 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. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. 3D pharmacophore models are three-dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation.
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