Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. Compared with traditional experimental high-throughput screening (HTS), VS is a more direct and rational drug discovery approach and has the advantage of low cost and effective screening. Virtual Screening can be used in retrospective studies or prospective studies.
In prospective studies, VS techniques provide a quick and economical method for the discovery of novel actives by selecting large database compounds for screening (experimental confirmation). Further, for smaller companies without access to routine high throughput screening (HTS) resource, such methods are crucial in the selection of molecules for internal screening. Refinements and optimization of high-throughput docking methods have led to improvements in reproducing experimental data and in hit rates obtained, validating their use in hit identification. Typical hit rates from experimental HTS can range between 0.01% and 0.14%, while hit rates for prospective virtual screening typically range between 1% and 40%.
Product Target Company
AG85, AG337, AG331
HIV Proteolytic Enzyme
Merck Sharp and Dohme(Harlow, UK)
Biota (Melbourne, Australia)
Roche (Basel, Switzerland)
Agouron (La Jolla, CA, USA)
TargetMol has the strength in computer assisted drug design and can provide the clients with virtual screening services based on structures of targets or small molecules against our chemical database to select potential compounds with high likelihood to be active in later experimental testing.
Molecular docking-based virtual screening
Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. Docking aims to predict binding modes and affinity of a small molecule within the binding site of the receptor target of interest, supporting the researcher in the understanding of the main physicochemical features related to the binding process. Docking methods fit a ligand into a binding site by combining and optimizing variables like steric, hydrophobic and electrostatic complementarity and also estimating the free energy of binding (scoring) to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets. Docking can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. Molecular docking-based virtual screening (DBVS) has become an increasingly important and essential tool for drug discovery. Application of DBVS has led to the identification of many new active compounds with novel molecular entity.
We usually take 3 rounds of screening procedures to ensure the accuracy of results: 1). Drug-likeness screening; 2) Docking-based virtual screening; 3). Manual screening by experienced scientists in drug discovery.
Manual screening is to select hits with high binding affinity based on molecular docking scores while taking 3D structural characteristics (hydrogen bond, hydrophobic and hydrophilic properties, etc.) of drug binding site into consideration. In reference to the structure activity relationships of existing drugs, efforts are made to keep the hits with important and conservative molecular interactions, as well as expand more novel core structure compounds into the list. Then cluster analysis will be performed to select the top ranked hits for later experimental testing.
Pharmacophore-based virtual screening
Pharmacophore-based virtual screening (PBVS) is a ligand-based drug design, nowadays a mature technology, very well accepted in the medicinal chemistry laboratory. Fundamentally, the core idea is based on building a pharmacophore model which is ‘the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response’. Because of their abstract nature and simplicity, 3D pharmacophore models represent efficient filters for the virtual screening of large compound libraries. PBVS is usually used as a complementary approach to DBVS for pre-processing databases (libraries) of small molecules to remove compounds not possessing features known to be essential for binding or for post-filtering compounds selected by docking approaches.
In the course of a PBVS run, a shared feature pharmacophore model is constructed from the active conformation of 1 or multiple active small molecules provided by clients. Then this pharmacophore model is screened against large chemical libraries, and molecules mapping the model are collected in a virtual hit list. These molecules fulfill the requirements of the model and therefore have a high likelihood to be active in the experimental testing. Some software for pharmacophore mapping can provide methods for quantitative prediction of activity such as 3D QSAR pharmacophore models.