Next generation 3D pharmacophore modeling. (26th February 2020)

• Record Type: Journal Article
• Authors: Schaller, David; Šribar, Dora; Noonan, Theresa; Deng, Lihua; Nguyen, Trung Ngoc; Pach, Szymon; Machalz, David; Bermudez, Marcel; Wolber, Gerhard
• Is Part Of: Wiley interdisciplinary reviews. Volume 10:Number 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-26
• Subjects: 3D pharmacophores -- artifical intelligence -- machine learning -- virtual screening -- web services; Chemistry, Physical and theoretical -- Periodicals; Cheminformatics -- Periodicals; Biochemistry -- Periodicals; 541.220285
• DOI: 10.1002/wcms.1468
• Languages: English
• ISSNs: 1759-0876

Abstract

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 Abstract : 3D pharmacophores have become an essential technique for in silico drug discovery. Recent algorithmic advances with respect to machine learning and molecular dynamics simulations as well as increased availability of computing resources allowed the evolution of classic pharmacophore modeling techniques toward powerful flexibility‐ and knowledge‐enriched techniques.