Abstract
Abstract3D 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
Highlights
Macromolecular biological structures such as proteins or DNA bind small organic molecules triggering functional modulation and biological response
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.[1]
We have given an overview of the principles of 3D pharmacophores and their role in drug discovery
Summary
Macromolecular biological structures such as proteins or DNA bind small organic molecules triggering functional modulation and biological response. Twenty selected hit molecules were further prioritized by previously reported structure-based sHE models and resulted in one novel and potent dual FLA protein/sHE inhibitor.[61] Since multitarget approaches are getting more and more attention, this example shows that ligand-based pharmacophores could have some benefits when applying on multiple targets that bind chemically similar physiological ligands. We introduce advanced approaches that integrate conformations from MD simulations, employ machine learning algorithms, and provide access to 3D pharmacophore searches without the requirement of expensive licenses and high-performance computers (Table 2) Since both macromolecules and ligands are dynamic entities, it becomes apparent that this holds true for macromolecule–ligand complexes and the underlying interactions. The import of 3D pharmacophore models in several data formats is supported
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