The structural similarity software vROCS OpenEye has

The structural similarity software vROCS (OpenEye) [45] has been utilized by Musumeci et al. [46] to screen the Maybridge [47] HitFinder database (∼14,400 compounds) using Distamycin A (Fig. 3B) as a query. Using the Tanimoto coefficient (Section 6.4) and vROCS's colour scoring (atom/feature similarity) criteria the authors discovered a set of novel G-quadruplex groove-binding ligands (Fig. 5A–C). These ligands bound with higher affinity to the grooves of human telomeric quadruplexes over dsDNA (detected by UV–Vis, fluorescence, and oligo affinity support analysis [46]) but had no observable melting temperature (Tm) shift. It was also shown that 3 of the 7 compounds induced a DNA damage response at the telomeres, further confirming their G4 binding activity. While this isn't the first reported campaign using vROCS in G4 drug discovery [48] it is a proof-of-concept that this relatively straight forward lead-discovery approach can enrich for novel scaffolds which interact in a favorable manner.
Libraries Arguably the most important consideration in virtual screening methodologies is the selection of naloxone hcl library. VS libraries contain hundreds to millions of virtual compounds that will inevitably dictate the scaffold diversity of resultant hits. The benefit of using large, diverse libraries is the expanded chemical search space. Fortunately, there are many large libraries available: MayBridge [47], AnalytiCon [49], ZINC [50], ChemDiv [51], SPECS [52], Mcule [53], eMolecules [54], PubChem [55], Life Chemicals [56], ChemBridge [57]. Some databases, such as the ZINC database, offer sub-libraries for a more tailored search (e.g. lead-like, fragment-like, drug-like, and natural products) which often contain readily purchasable or synthesized compounds. Conversely, some researchers choose to develop their own curated libraries [42,58] which can be beneficial when the user has limited computing resources available. The biggest challenge is finding the optimal balance among speed, accuracy, and library composition. A library of ∼1 million compounds docked to a single receptor will take as little as weeks to as much as a year of computing time with a single workstation using a rigorous algorithm. Many researchers have circumvented this by reducing libraries to smaller, more manageable subsets which only contain compounds that conform to a predefined criterion. Specifically, using a shape-based or pharmacophore search on a library one can significantly reduce the size and enrich for chemical moieties that are well suited to the system of interest (see Refs. [42,46,48,[59], [60], [61]]). However, limiting searches to a pre-defined chemical search space introduces significant bias and is bound to limit compound diversity. Alternatively, increased computing power by use of a research cluster or computing grid can greatly reduce the computational time required for a screening campaign of >1 million compounds [62]. The authors have had success using grid computing which can dock as many as ∼25 million compounds in just a few days to a single receptor site [31]. While grid computing is becoming more commonplace in research institutes, not everyone has access to large scale grids. Therefore, care must be taken if curating a library to be docked at a smaller scale. Hand picking small subsets of compounds can lead to significant bias (Section 6), or worse, no enrichment of meaningful hits [63].
Docking Docking has been in use since the early 80's and has gained traction commensurate to the number of published protein and nucleic acid structures since [64]. In general, docking seeks to use the physical and chemical information provided by an atomistic receptor to dock whole or fragmented molecules from a library and rank them using a scoring function. Each docking platform has its own algorithm as well as flavor of scoring function, which has made cross-platform comparisons difficult [[65], [66], [67]]. The lack of convergence onto any one platform is likely due to the unique features inherent to each, such as: cost, speed, scoring terms, ease-of-use, scalability, receptor flexibility, ligand flexibility, and the option of implementing molecular dynamics (MD) force fields.