Molecular docking and structure-based drug design strategies

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

1,4-Dihydropyridines as calcium channel ligands and privileged structures

1. The 1,4-dihydropyridine nucleus serves as the scaffold for important cardiovascular drugs-calcium antagonists-including nifedipine, nitrendipine, amlodipine, and nisoldipine, which exert their antihypertensive and antianginal actions through actions at voltage-gated calcium channels of the CaV1 (L-type) class. 2. These drugs act at a specific receptor site for which defined structure-activity relationships exist, including stereoselectivity. 3. Despite the widespread occurrence of the CaV1 class of channel, the calcium antagonists exhibit significant selectivity of action in the cardiovascular system. This selectivity arises from a number of factors including subtype of channel, state-dependent interactions. pharmacokinetics, and mode of calcium mobilization. 4. The 1,4-dihydropyridine nucleus is also a privileged structure or scaffold that can, when appropriately decorated substituents, interact at diverse receptors and ion channels, including potassium and sodium channels and receptors of the G-protein class.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

Sigma-2 receptor ligands and their perspectives in cancer diagnosis and therapy

The sigma-2 receptor is highly expressed in various rapidly proliferating cancer cells and regarded as a cancer cell biomarker. Selective sigma-2 ligands have been shown to specifically label the tumor sites, induce cancer cells to undergo apoptosis, and inhibit tumor growth. Sigma-2 ligands are potentially useful as cancer diagnostics, anticancer therapeutics, or adjuvant anticancer treatment agents. However, both the cloning of this receptor and the identification of its endogenous ligand have not been successful, and the lack of structural information has severely hindered the understanding of its physiological roles, its signaling pathways, and the development of more selective sigma-2 ligands. Recent data have implicated that sigma-2 binding sites are within the lipid rafts and that PGRMC1 (progesterone receptor membrane component 1) complex and sigma-2 receptor may be coupled with EGFR (epidermal growth factor receptor), mTOR (mammalian target of rapamycin), caspases, and ion channels. Due to its promising applications in cancer management, there are rapidly increasing research efforts that are being directed into this field. This review article updates the current understanding of sigma-2 receptor and its potential physiological roles, applications, interaction with other effectors, with special focuses on the development of sigma-2 ligands, their chemical structures, pharmacological profiles, applications in imaging and anticancer therapy.

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

The importance of discerning shape in molecular pharmacology

Shape is a fundamentally important molecular feature that often determines the fate of a compound in terms of molecular interactions with preferred and non-preferred biological targets. Complementarity of binding in small-molecule-protein, peptide-receptor, antigen-antibody and protein-protein interactions is the key to life and survival and also to targeting molecules with bioactivity. We review the application of shape in various biological systems such as substrate recognition, ligand specificity or selectivity and antibody recognition in the context of computational methods such as docking, quantitative structure-activity relationships, classification models and similarity-search algorithms. These in silico pharmacology methods have recently demonstrated the importance and applicability of determining molecular shape in drug discovery, virtual screening and predictive toxicology. The results from recently published studies show that shape and shape-based descriptors are at least as useful as other traditional molecular descriptors.

Strategies To Design Selective Histone Deacetylase Inhibitors

This review classifies drug-design strategies successfully implemented in the development of histone deacetylase (HDAC) inhibitors, which have many applications including cancer treatment. Our focus is on especially demanded selective HDAC inhibitors and their structure-activity relationships in relation to corresponding protein structures. The main part of the paper is divided into six subsections each narrating how optimization of one of six structural features can influence inhibitor selectivity. It starts with the impact of the zinc binding group on selectivity, continues with the optimization of the linker placed in the substrate binding tunnel as well as the adjustment of the cap group interacting with the surface of the protein, and ends with the addition of groups targeting class-specific sub-pockets: the side-pocket-, lower-pocket- and foot-pocket-targeting groups. The review is rounded off with a conclusion and an outlook on the future of HDAC inhibitor design.

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.

Pharmacophore based virtual screening, molecular docking, molecular dynamics and MM-GBSA approach for identification of prospective SARS-CoV-2 inhibitor from natural product databases

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily appeared in Wuhan, China, in December 2019. At present, no proper therapy and vaccinations are available for the disease, and it is increasing day by day with a high mortality rate. Pharmacophore based virtual screening of the selected natural product databases followed by Glide molecular docking and dynamics studies against SARS-CoV-2 main protease was investigated to identify potential ligands that may act as inhibitors. The molecules SN00293542 and SN00382835 revealed the highest docking score of −14.57 and −12.42 kcal/mol, respectively, when compared with the co-crystal ligands of PDB-6Y2F (O6K) and 6W63 (X77) of the SARS-CoV-2 M^pro. To further validate the interactions of top scored molecules SN00293542 and SN00382835, molecular dynamics study of 100 ns was carried out. This indicated that the protein-ligand complex was stable throughout the simulation period, and minimal backbone fluctuations have ensued in the system. Post-MM-GBSA analysis of molecular dynamics data showed free binding energy-71.7004 +/− 7.98, −56.81+/− 7.54 kcal/mol, respectively. The computational study identified several ligands that may act as potential inhibitors of SARS-CoV-2 M^pro. The top-ranked molecules SN00293542, and SN00382835 occupied the active site of the target, the main protease like that of the co-crystal ligand. These molecules may emerge as a promising ligands against SARS-CoV-2 and thus needs further detailed investigations.

Communicated by Ramaswamy H. Sarma

Common mechanisms of inhibition for the Na+/glucose (hSGLT1) and Na+/Cl-/GABA (hGAT1) cotransporters

1. Electrophysiological methods were used to investigate the interaction of inhibitors with the human Na(+)/glucose (hSGLT1) and Na(+)/Cl(-)/GABA (hGAT1) cotransporters. Inhibitor constants were estimated from both inhibition of substrate-dependent current and inhibitor-induced changes in cotransporter conformation. 2. The competitive, non-transported inhibitors are substrate derivatives with inhibition constants from 200 nM (phlorizin) to 17 mM (esculin) for hSGLT1, and 300 nM (SKF89976A) to 10 mM (baclofen) for hGAT1. At least for hSGLT1, values determined using either method were proportional over 5-orders of magnitude. 3. Correlation of inhibition to structure of the inhibitors resulted in a pharmacophore for glycoside binding to hSGLT1: the aglycone is coplanar with the pyranose ring, and binds to a hydrophobic/aromatic surface of at least 7x12A. Important hydrogen bond interactions occur at five positions bordering this surface. 4. In both hSGLT1 and hGAT1 the data suggests that there is a large, hydrophobic inhibitor binding site approximately 8A from the substrate binding site. This suggests an architectural similarity between hSGLT1 and hGAT1. There is also structural similarity between non-competitive and competitive inhibitors, e.g., phloretin is the aglycone of phlorizin (hSGLT1) and nortriptyline resembles SKF89976A without nipecotic acid (hGAT1). 5. Our studies establish that measurement of the effect of inhibitors on presteady state currents is a valid non-radioactive method for the determination of inhibitor binding constants. Furthermore, analysis of the presteady state currents provide novel insights into partial reactions of the transport cycle and mode of action of the inhibitors.

3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating "Hypo1", it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC(50)) data thus successfully validating "Hypo1" by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors.

Investigation of the pharmacophore space of Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) NTPase/helicase by dihydroxychromone derivatives

Aryl diketoacids have been identified as the first SARS-CoV NTPase/helicase inhibitors with a distinct pharmacophore featuring an arylmethyl group attached to a diketoacid. In order to search for the pharmacophore space around the diketoacid core, three classes of dihydroxychromone derivatives were prepared. Based on SAR study, an extended feature of the pharmacophore model of SARS-CoV NTPase/helicase was proposed which is constituted of a diketoacid core, a hydrophobic arylmethyl substituent, and a free catechol unit.

Pyrrolo[2,1-a]isoquinoline scaffold in drug discovery: advances in synthesis and medicinal chemistry

Pyrrolo[2,1-a]isoquinoline (PIq) is a nitrogen heterocyclic scaffold of diverse alkaloids endowed with several biological activities, including antiretroviral and antitumor activities. Several 5,6-dihydro-PIq (DHPIq) alkaloids, belonging to the lamellarins' family, have proved to be cytotoxic to tumor cells, as well as reversers of multidrug resistance. In this review, we provide an overview of the main achievements over the last decade in the synthetic approaches to access libraries of PIq compounds along with a survey, as comprehensive as possible, of bioactivity, mechanism of action, pharmacophore and structure-activity relationships of synthetic analogs of DHPIq-based alkaloids. The focus is mainly on the potential exploitation of the (DH)PIq scaffold in design and development of novel antitumor drugs.

Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel Akt2 inhibitors

Akt2 is considered as a potential target for cancer therapy. In order to find novel Akt2 inhibitors which have different scaffolds, structure-based pharmacophore model and 3D-QSAR pharmacophore model were built and validated by different methods. Then, they were used for chemical databases virtual screening. The selected compounds were further analyzed and refined using drug-like filters and ADMET analysis. Finally, seven hits with different scaffolds were picked out for docking studies. These seven hits were predicted to have high inhibitory activity and good ADMET properties, they may act as novel leads for Akt2 inhibitors designing.

Pharmacophore-based virtual screening of catechol-o-methyltransferase (COMT) inhibitors to combat Alzheimer's disease

Alzheimer's disease (AD) is one of the most significant neurodegenerative disorders and its symptoms mostly appear in aged people. Catechol-o-methyltransferase (COMT) is one of the known target enzymes responsible for AD. With the use of 23 known inhibitors of COMT, a query has been generated and validated by screening against the database of 1500 decoys to obtain the GH score and enrichment value. The crucial features of the known inhibitors were evaluated by the online ZINC Pharmer to identify new leads from a ZINC database. Five hundred hits were retrieved from ZINC Pharmer and by ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering by using FAF-Drug-3 and 36 molecules were considered for molecular docking. From the COMT inhibitors, opicapone, fenoldopam, and quercetin were selected, while ZINC63625100_413 ZINC39411941_412, ZINC63234426_254, ZINC63637968_451, and ZINC64019452_303 were chosen for the molecular dynamics simulation analysis having high binding affinity and structural recognition. This study identified the potential COMT inhibitors through pharmacophore-based inhibitor screening leading to a more complete understanding of molecular-level interactions.

Applications of the Pharmacophore Concept in Natural Product inspired Drug Design

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.

Boronic acid with high oxidative stability and utility in biological contexts

Despite their desirable attributes, boronic acids have had a minimal impact in biological contexts. A significant problem has been their oxidative instability. At physiological pH, phenylboronic acid and its boronate esters are oxidized by reactive oxygen species at rates comparable to those of thiols. After considering the mechanism and kinetics of the oxidation reaction, we reasoned that diminishing electron density on boron could enhance oxidative stability. We found that a boralactone, in which a carboxyl group serves as an intramolecular ligand for the boron, increases stability by 10⁴-fold. Computational analyses revealed that the resistance to oxidation arises from diminished stabilization of the p orbital of boron that develops in the rate-limiting transition state of the oxidation reaction. Like simple boronic acids and boronate esters, a boralactone binds covalently and reversibly to 1,2-diols such as those in saccharides. The kinetic stability of its complexes is, however, at least 20-fold greater. A boralactone also binds covalently to a serine side chain in a protein. These attributes confer unprecedented utility upon boralactones in the realms of chemical biology and medicinal chemistry.

Carboranes as unique pharmacophores in antitumor medicinal chemistry

Carborane is a carbon-boron molecular cluster that can be viewed as a 3D analog of benzene. It features special physical and chemical properties, and thus has the potential to serve as a new type of pharmacophore for drug design and discovery. Based on the relative positions of two cage carbons, icosahedral closo-carboranes can be classified into three isomers, ortho-carborane (o-carborane, 1,2-C2B10H12), meta-carborane (m-carborane, 1,7-C2B10H12), and para-carborane (p-carborane, 1,12-C2B10H12), and all of them can be deboronated to generate their nido- forms. Cage compound carborane and its derivatives have been demonstrated as useful chemical entities in antitumor medicinal chemistry. The applications of carboranes and their derivatives in the field of antitumor research mainly include boron neutron capture therapy (BNCT), as BNCT/photodynamic therapy dual sensitizers, and as anticancer ligands. This review summarizes the research progress on carboranes achieved up to October 2021, with particular emphasis on signaling transduction pathways, chemical structures, and mechanistic considerations of using carboranes.

Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor

Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs.

Elfaky M. Repurposing of Some Natural Product Isolates as SARS-COV-2 Main Protease Inhibitors via In Vitro Cell Free and Cell-Based Antiviral Assessments and Molecular Modeling Approaches

The emergence of the SARS-CoV-2 pandemic has prompted scientists to search for an efficient antiviral medicine to overcome the rapid spread and the marked increase in the number of patients worldwide. In this regard natural products could be a potential source of substances active against coronavirus infections. A systematic computer-aided virtual screening approach was carried out using commercially available natural products found on the Zinc Database in addition to an in-house compound library to identify potential natural product inhibitors of SARS-CoV-2 main protease (MPRO). The top eighteen hits from the screening were selected for in vitro evaluation on the viral protease (SARS-CoV-2 MPRO). Five compounds (naringenin, 2,3',4,5',6-pentahydroxybenzophenone, apigenin-7-O-glucoside, sennoside B, and acetoside) displayed high activity against the viral protein. Acteoside showed similar activity to the positive control GC376. The most potent compounds were tested in vitro on SARS-CoV-2 Egyptian strain where only naringenin showed moderate anti-SARS-CoV-2 activity at non-cytotoxic micromolar concentrations in vitro with a significant selectivity index (CC50/IC50 = 178.748/28.347 = 6.3). Moreover; a common feature pharmacophore model was generated to explain the requirements for enzyme inhibition by this diverse group of active ligands. These results pave a path for future repurposing and development of natural products to aid in the battle against COVID-19.

Discovery of a novel IKK-β inhibitor by ligand-based virtual screening techniques

Various inflammatory stimuli that activate the nuclear factor kappa B (NF-κB) signaling pathway converge on a serine/threonine kinase that displays a key role in the activation of NF-κB: the I kappa B kinase β (IKK-β). Therefore, IKK-β is considered an interesting target for combating inflammation and cancer. In our study, we developed a ligand-based pharmacophore model for IKK-β inhibitors. This model was employed to virtually screen commercial databases, giving a focused hit list of candidates. Subsequently, we scored by molecular shape to rank and further prioritized virtual hits by three-dimensional shape-based alignment. One out of ten acquired and biologically tested compounds showed inhibitory activity in the low micromolar range on IKK-β enzymatic activity in vitro and on NF-κB transactivation in intact cells. Compound 8 (2-(1-adamantyl)ethyl 4-[(2,5-dihydroxyphenyl)methylamino]benzoate) represents a novel chemical class of IKK-β inhibitors and shows that the presented model is a valid approach for identification and development of new IKK-β ligands.

Homology modeling, docking and structure-based virtual screening for new inhibitor identification of Klebsiella pneumoniae heptosyltransferase-III

Klebsiella pneumoniae (K. pneumoniae) is a Gram-negative opportunistic pathogen commonly associated with hospital-acquired infections that are often resistant even to antibiotics. Heptosyltransferase (HEP) belongs to the family of glycosyltransferase-B (GT-B) and plays an important in the synthesis of lipopolysaccharides (LPS) essential for the formation of bacterial cell membrane. HEP-III participates in the transfer of heptose sugar to the outer surface of bacteria to synthesize LPS. LPS truncation increases the bacterial sensitivity to hydrophobic antibiotics and detergents, making the HEP as a novel drug target. In the present study, we report the 3D homology model of K. pneumoniae HEP-III and its structure validation. Active site was identified based on similarities with known structures using Dali server, and structure-based pharmacophore model was developed for the active site substrate ADP. The generated pharmacophore model was used as a 3D search query for virtual screening of the ASINEX database. The hit compounds were further filtered based on fit value, molecular docking, docking scores, molecular dynamics (MD) simulations of HEP-III complexed with hit molecules, followed by binding free energy calculations using Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA). The insights obtained in this work provide the rationale for design of novel inhibitors targeting K. pneumoniae HEP-III and the mechanistic aspects of their binding. Communicated by Ramaswamy H. Sarma.

Dynamic structure-based pharmacophore model development: a new and effective addition in the histone deacetylase 8 (HDAC8) inhibitor discovery

Histone deacetylase 8 (HDAC8) is an enzyme involved in deacetylating the amino groups of terminal lysine residues, thereby repressing the transcription of various genes including tumor suppressor gene. The over expression of HDAC8 was observed in many cancers and thus inhibition of this enzyme has emerged as an efficient cancer therapeutic strategy. In an effort to facilitate the future discovery of HDAC8 inhibitors, we developed two pharmacophore models containing six and five pharmacophoric features, respectively, using the representative structures from two molecular dynamic (MD) simulations performed in Gromacs 4.0.5 package. Various analyses of trajectories obtained from MD simulations have displayed the changes upon inhibitor binding. Thus utilization of the dynamically-responded protein structures in pharmacophore development has the added advantage of considering the conformational flexibility of protein. The MD trajectories were clustered based on single-linkage method and representative structures were taken to be used in the pharmacophore model development. Active site complimenting structure-based pharmacophore models were developed using Discovery Studio 2.5 program and validated using a dataset of known HDAC8 inhibitors. Virtual screening of chemical database coupled with drug-like filter has identified drug-like hit compounds that match the pharmacophore models. Molecular docking of these hits reduced the false positives and identified two potential compounds to be used in future HDAC8 inhibitor design.