3D Pharmacophore models are three-dimensional ensembles of chemically specified interactions of a ligand in its bioactive state. They are now an important technique in drug design because they offer a sophisticated method of deciphering chemically stored ligand information. The important research for using 3D pharmacophore models in high - throughput screening and mechanistic investigations for protein functioning are summarised in this review, along with an outline of the method's fundamental principles. We also go over current advancements in the area. Since these methods take into account macromolecule-ligand interactions as dynamic and hence display an interaction pattern that is physiologically accurate, combining 3D pharmacophore models with molecular dynamics simulations might be a huge step forward. The effective use of 3D pharmacophore data in artificial intelligence and machine learning is another trend. Other themes include using 3D pharmacophore data effectively in machine learning and artificial intelligence applications or providing 3D pharmacophore modelling on publicly available web servers. Recent advancements demonstrate that 3D pharmacophore modelling is a dynamic area with many implications in drug discovery and beyond.
Yong Xiao Wang
Albany Medical College, United States
Consolato M Sergi
Universities of Alberta and Ottawa, Canada
Vladlen Slepak
University of Miami Miller School of Medicine, United States
Andreas M Papas
Antares Health Products, United States
Axel H Schonthal
University of Southern California, United States
Bernd Blobel
University of Regensburg, Germany
Gurpreet Singh
IQVIA, United Kingdom
Rassa Pegahi
UPSA, France
Tanuja Tummala
Harrisburg University of Science and Technology, United States
Natassa Pippa
University of Athens, GreeceSubmit your abstract Today
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