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  • VS strategies have been building momentum in G drug discover


    VS strategies have been building momentum in G4 drug discovery both as a low-cost enrichment step and as a lead development step in the discovery pipeline, which our laboratory has previously discussed [31]. Whereas traditional HTS methods rely on obtaining and screening hundreds or thousands of compounds from curated libraries, VS simply requires knowledge of known ligand structures (for similarity and pharmacophore searches) or a rifampicin mg structure to which a library of virtual compounds can be docked. These methods are known as ligand-based or receptor-based drug discovery, respectively. Ligand-based methods use an identified set of know active ligands to search a database for compounds which have similar properties. These techniques operate under the assumption that ligands with a similar 2D or 3D structure will offer similar interactions with their targets. Conversely, receptor-based methods screen virtual libraries against a target structure, and so require an X-ray crystallographic, NMR, or homology-derived 3D atomistic model of the target. These coordinate files can be downloaded from databases such as the Protein Data Bank (PDB) (>133,000) or the Nucleic Acid Database (NAD) (>900), which are continuously being updated with new structures. VS platforms have been extensively used in ligand discovery [32,33], however, until now there has not been an assessment of strategies specifically targeting G4s. Here we briefly discuss some of the common screening strategies, such as docking and pharmacophore screening, as well as relevant aspects including: library preparation, scoring, and analysis. This is followed with a commentary on suggested best practices for in silico G4 drug discovery based on the authors' own experience and knowledge gleaned from successful campaigns.
    Pharmacophore & similarity based screening Ligand-based methods such as pharmacophore and similarity search rifampicin mg platforms are widely used and often integrated into a VS docking campaign pre- and/or post-docking (Section 4). The term ‘pharmacophore’ as we use it refers to an abstract, 3D physio-chemical representation of the chemical moieties necessary for ligand-receptor interaction. Pharmacophore screens use multiple ligands of the same binding site to derive an ensemble of chemical features necessary for an ideal interaction (i.e. hydrogen bond donor/acceptor, aromatic ring elements, cations, anions, etc.). The resulting model is known as a hypothesis. These hypotheses, which are 3D chemical descriptors, are then used to screen a virtual library to find “pharmacophore-similars” that satisfy the hypothesis [34]. The result is a list of compounds which are ranked for their probability of favorable interactions based on their physical and chemical similarity to the initial query structure. Various pharmacophore search platforms are available such as Pharmer (ZINC) [35], Discovery Studio's 3D-QSAR module (Accelrys) [36], LigandScout (Inteligand) [37], MOE (Chemical Computing Group) [38], Phase (Schrӧdinger) [39], SYBYL-X2.1.1 (Certera) [40], and Pharao (Silicos) [41]. An example of a successful G4 pharmacophore screening campaign comes from Chen et al. [42] in which the authors used Discovery Studio's 3D-QSAR pharmacophore generation module to construct a model based on acridine derivatives. By weighting hydrophobic interactions higher than aromatic interactions in the hypothesis the authors enriched for compounds with scaffolds unlike the acridines. This was achieved by screening their own in-house library. The resulting compound was a triaryl-substituted imidazole derivative (Fig. 4A) which had a Kd of 0.5 μM against a human telomere G4 and displayed selectivity over dsDNA based on circular dichroism (CD) and fluorescence melting experiments. Interestingly, this compound is very similar to the triarylpyridines discovered previously [43] (Fig. 4B). The second and most rapid ligand-based strategy is known as a structural similarity search. These platforms require only knowledge of active ligand's chemical composition (i.e. a chemical structure). In the past, this approach utilized a rigid-body alignment approach using 2D (2-dimensional) and 3D chemical fingerprints to align and rank each molecule. This was enhanced with the advent of semi-flexible and flexible superposition algorithms that allow for a more comprehensive search in 3D space by ranking each molecule based on the volume overlap within the query structure. See Ref. [44] for more in-depth discussion.