Virtual high throughput screening (vHTS) – A perspective

Abstract

Abstract:With the exponential rise in the number of viable novel drug targets, computational methods are being increasingly applied to accelerate the drug discovery process. Virtual High Throughput Screening (vHTS) is one such established methodology to identify drug candidates from large collection of compound libraries. Although it complements the expensive and time consuming High Throughput Screening (HTS) of compound libraries, vHTS possess inherent challenges. The successful vHTS requires the careful implementation of each phase of computational screening experiment right from target preparation to hit identification and lead optimization. This article discusses some of the important considerations that are imperative for designing a successful vHTS experiment.Keywords: virtual high throughput screening; receptor based and ligand based screening; homology models; chemical databases; ADME filters; toxicity filters Background:The discovery of novel drug targets has increased exponentially in recent years due to advances in genomic and molecular biology techniques. Experimental and computational methods are effectively applied to accelerate the process of lead identification and optimization. The chemical leads are small potential drug like molecules which are capable of modulating the function of the target proteins that are further optimized to act as a therapeutic drug against a targeted disease. Conventional experimental methods like High Throughput Screening (HTS) continue to be the best method for rapid identification of drug leads. HTS identifies lead molecules by performing individual biochemical assays with over millions of compounds. However, the huge cost and time consumed with this technology has lead to the integration of cheaper and effective computational methodology namely virtual High Throughput Screening (vHTS). vHTS is a computational screening method which is widely applied to screen insilico collection of compound libraries to check the binding affinity of the target receptor with the library compounds [1]. This is achieved by using a scoring function which computes the complementarity of the target receptor with the compounds. HTS and vHTS are complementary methods [2] and vHTS has been shown to reduce false positives in HTS [3]. Several vHTS strategies have been practiced [4] and the technique is being continuously optimized for better performance.

Using Fragment Based Drug Discovery to Target Epigenetic Regulators in Cancer

“…Epigenetic enzymes are more traditional drug targets and inhibitors can be developed through highly established processes such as high throughput screening (HTS) [15], in silico screening [16], and rational structural-based drug design [17]. In HTS, a collection of ~1 million compounds is screened against the protein target mostly by an enzymatic assay and less frequently by simple biophysical binding assays (ex.…”

“…thermal shift, surface plasmon resonance) [15]. A well-resolved protein structure from X-ray crystal diffraction or NMR analysis is generally required for in silico screening [16]. Using specialized software, large libraries of commercially-available compounds can be virtually docked one-by-one into the target structure's active site [16].…”

“…A well-resolved protein structure from X-ray crystal diffraction or NMR analysis is generally required for in silico screening [16]. Using specialized software, large libraries of commercially-available compounds can be virtually docked one-by-one into the target structure's active site [16]. Subsequent to the computational screen, hits can be purchased or synthesized and then assayed for activity against the epigenetic enzymatic target.…”

Discovery of Some Potent PDHK Inhibitors Using Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies

“…The developed pharmacophore model was then used to virtually screen a library of known and commercially available drug molecules to find out more potent and non-toxic PDHK inhibitors. The chosen chemical database has been successfully screened to identify hits through computer-aided screening or virtual screening, which reduces the time and cost of research effectively [15][16][17][18][19] . The intensity with which a particular molecule can inhibit PDHK2 has been judged on the basis of two important criteria: fitness to the pharmacophore and 3D-QSAR model AHH.2 and good binding affinity within the PDHK active site.…”

A multibody atomic statistical potential for the prediction of enzyme-inhibitor binding energy

“…Though the HTS process has the potential to be time consuming and expensive, the emergence of computer-based virtual HTS (vHTS) has led to more efficient identification of drug candidates from among a large collection of compounds [2]. A variety of computational tools have also been developed for estimating enzyme-inhibitor binding energy based on scoring functions that take into account physicochemical interactions, including X-Score [3], LigScore [4], DrugScore [5], SFCscore [6], AutoDock4 [7], ITScore [8,9], and PHOENIX [10].…”