About this Research Topic
In the current drug research environment in academia and industry, cheminformatics and virtual screening methods are well established and integrated tools. Computational tools are used to predict a compound's 3D structure, the 3D structure and function of a pharmacological target, ligand-target interactions, binding energies, and other factors essential for a successful drug. This includes molecular properties such as solubility, logP value, susceptibility to metabolism, cell permeation, blood brain barrier permeation, interaction with drug transporters and potential off-target effects. Given that approximately 40 million unique compounds are readily available for purchase, such computational modeling and filtering tools are essential to support the drug discovery and development process. The aim of all these calculations is to focus experimental efforts on the most promising candidates and exclude problematic compounds early in the project.
In this Research Topic on virtual activity predictions, we will cover several aspects of this research area such as historical perspectives, data sources, ligand treatment, virtual screening methods, hit list handling and filtering. Several success stories will illustrate ‘good modeling practise’ examples. Although these computational models can be applied very successfully in many steps of the drug discovery and development process, researchers must apply them with caution and should not have unrealistic expectations.
This article collection will give researchers an overview of currently used activity prediction methods, their applications, related issues, and provide best practice guidance to perform in silico experiments.
Keywords: Drug Discovery, Virtual Screening, Activity Profiling, Target Prediction, Molecular Modeling
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