Virtual screening filters for the design of type II p38 MAP kinase inhibitors: a fragment based library generation approach

Novel quinolone substituted 1,3,4-oxadiazole derivatives: design, synthesis, antimicrobial and anti-inflammatory potential

A novel series of quinolone-substituted 1,3,4-oxadiazole derivatives 4(a-l) have been designed and synthesized. The target compounds were investigated for their antibacterial activity against gram positive (Staphylococcus aureus, ATCC 25923, Enterococcus faecalis, ATCC 29212) and gram negative bacterium (Escherichia coli, ATCC 25922, Pseudomonas aeruginosa, ATCC 27853) for antifungal activity using (Candida albicans, ATCC 10231) and anti-inflammatory activity as COX-II inhibitors, respectively. The 1,3,4-oxadiazole functionality was introduced at C-6 position of pipemidic acid derivatives. IR, 1H NMR and Mass spectrometry techniques confirmed the structure of synthesized derivatives. The quinolone (pipemidic acid)-oxadiazole hybrid derivatives were effective against bacterial strains. When compared to ciprofloxacin (MIC 16 µg/mL), the compounds under consideration (4f, 4h, and 4k) showed significant antibacterial activity against all bacterial strains except Enterococcus faecalis, with MICs of 8 µg/mL. On the other hand, synthesized target compounds 4(a-l) did not respond well against Candida albicans fungal strain. The compound (4k) represents high % inhibition against COX-II. The compounds (4f, 4h and 4k) exhibited highest hydrogen bonding interaction with ARG57, ARG72, ARG78, LEU54 and MET16 target residues with a binding energy of - 8.4, - 8.6 and - 8.5 kcal/mol into the active pocket of DNA gyrase enzyme respectively even better in comparison to reference ligands. Based on the docking study, quinolone (pipemidic acid) oxadiazole hybrid structural ligands exhibited strong interaction at binding pockets of DNA gyrase enzyme.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space

Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put into 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-AadharID, for each of these 149,169,443 molecules. MPDS-AadharID is akin to the unique number given to citizens in India (similar to SSN in the US and NINO in the UK). The unique features of MPDS-CL are (a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; (b) automatic generation of information for the processing for MPDS and other galaxy tools; (c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and (d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at https://mpds.neist.res.in:8086/ .

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite

A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library. A systematic fragmentation method that breaks a given molecule into rings, linkers, and substituents was used to cleave the molecules and the fragments were analyzed. Further, only the ring framework was taken into the consideration to develop a fragment library that consists of a total number of 107,614 unique fragments. This set represents a rich diverse structure framework that covers a wide variety of yet-to-be-explored fragments for a wide range of small molecule-based applications. This fragment library is an integral part of the molecular property diagnostic suite (MPDS) suite that can be used with other modeling and informatics methods for FBDD approaches. The fragment library module of MPDS can be accessed at http://mpds.neist.res.in:8085.

Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing

Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR. PCA is applied on all the two dimensional (2D) molecular descriptors of the dataset and the first five Principal Components (PC) were subsequently used to cluster the drugs into nine well separated clusters using k-means algorithm. We further predicted the biological activities for the drug-dataset using the PASS (Predicted Activities Spectra of Substances) tool. These predicted activity values are analyzed systematically to identify repurposable drugs for various diseases. Clustering patterns obtained from k-means showed that every cluster contains subgroups of structurally similar drugs that may or may not have similar therapeutic indications. We hypothesized that such structurally similar but therapeutically different drugs can be repurposed for the native indications of other drugs of the same cluster based on their high predicted biological activities obtained from PASS analysis. In line with this, we identified 66 drugs from the nine clusters which are structurally similar but have different therapeutic uses and can therefore be repurposed for one or more native indications of other drugs of the same cluster. Some of these drugs not only share a common substructure but also bind to the same target and may have a similar mechanism of action, further supporting our hypothesis. Furthermore, based on the analysis of predicted biological activities, we identified 1423 drugs that can be repurposed for 366 new indications against several diseases. In this study, an integrated approach of unsupervised ML and SAR analysis have been used to identify new indications for approved drugs and the study provides novel insights into clustering patterns generated through descriptor level analysis of approved drugs.

Drug repositioning for anti-tuberculosis drugs: an in silico polypharmacology approach

Development of potential antitubercular molecules is a challenging task due to the rapidly emerging drug-resistant strains of Mycobacterium tuberculosis (M.tb). Structure-based approaches hold greater benefit in identifying compounds/drugs with desired polypharmacological profiles. These methods can be employed based on the knowledge of protein binding sites to identify the complementary ligands. In this study, polypharmacology guided computational drug repurposing approach was applied to identify potential antitubercular drugs. 20 important druggable protein targets in M.tb were considered from the target library of Molecular Property Diagnostic Suite-Tuberculosis (MPDS^TB- http://mpds.neist.res.in:8084 ) for virtual screening. FDA approved drugs were collected, preprocessed and docked in the active sites of the 20 M.tb targets. The top 300 drug molecules from each target (20 × 300) were filtered-in and subsequently screened for possible antitubercular and antimycobacterial activity using PASS tool. Using this approach, 34 drugs with predicted antitubercular and anti-mycobacterial activity were identified along with good binding affinity against multiple M.tb targets. Interestingly, 21 out of the 34 identified drugs are antibiotics while 4 drug molecules (nitrofural, stavudine, quinine and quinidine) are non-antibiotics showing promising predicted antitubercular activity. Most of these molecules have the similar privileged antimycobacterial drugs scaffold. Further drug likeness properties were calculated to get deeper insights to M.tb lead molecules. Interestingly, it was also observed that the drugs identified from the study are under different stages of drug discovery (i.e., in vitro, clinical trials) for the effective treatment of various diseases including cancer, degenerative diseases, dengue virus infection, tuberculosis, etc. Krasavin et al., 2017 synthesized nitrofuran analogues with appreciable MICs (22-23 µM) against M.tb H37Rv. These experiments further add to the credibility of the drugs identified in this study (TB).

Study of lipid heterogeneity on bilayer membranes using molecular dynamics simulations

Human cell membranes consist of various lipids that are essential for their structure and function. It typically comprises phosphatidylcholine (POPC), phosphatidylethanolamine (POPE), phosphatidylserine (POPS), sphingomyelin (PSM), and cholesterol (CHL). Several experimental and computational techniques have been employed to characterize the composition of human cell membranes, however, CHL enriched membrane is still not clearly understood through these techniques. Molecular dynamics simulation results illustrated the biophysical properties of heterogeneous membranes based on the lipid composition as well as the concentration of lipids, exclusively for CHL and PSM. Herein, we have investigated the structure-function relationships of lipids comparatively to delineate the effect of heterogeneity on the biophysical properties of different membranes. It has been observed that the significant fraction of CHL (i.e., ~33% in ternary, ~25% in quaternary, and ~16% in senary type bilayers) in combination with other lipids introduced compactness, and increased the thickness of the membrane. The analysis of lipid mass density stated that the density of lipid head group, phosphate_, and glycerol-ester in presence of CHL with or without PSM is an underlying reason for membrane ordering. Results also revealed that the presence of POPI and POPS are the reasons for an adequate drop in the ordering of lipid chain, particularly on POPE chain. The self-interaction of CHL, PSM, POPE and the interaction of CHL and POPC with POPE seem to determine the structure and function of the heterogeneous membrane. Our findings provide a qualitative understanding of the effect of membrane heterogeneity on the physiological properties of membranes. The structures inspected in this study would help to select the heterogeneous bilayer model to mimic the human cell membranes to analyse or characterize the membrane-associated phenomena.

Novel heterocyclic 1,3,4-oxadiazole derivatives of fluoroquinolones as a potent antibacterial agent: Synthesis and computational molecular modeling

Design and synthesis of novel series of 1,3,4-oxadiazoles containing FQs derivatives and screened their antibacterial, antimycobacterial properties. The synthesized compounds were characterized by different spectral techniques like IR, ¹H NMR, ¹³C NMR, mass and elemental analysis. The results of the antimicrobial activity and compounds 6d, 6b, 6e, 6f and 6a demonstrated potent antibacterial activities with zone of inhibition of 42, 36, 37, 34 and 30 mm against S. aureus, E. faecalis, S. pneumoniae, E. coli and K. pneumoniae, respectively. 1,3,4-Oxadiazole derivatives 6a, 6b, 6 g were showed excellent antimycobacterial activity against M. smegmatis H₃₇Rv with MICs 22.35, 16.20, 20.28 µg/mL, respectively. FQs 6d and 6b exhibited highest hydrogen bonding interactions with Asp83 (3.11 A˚), Ser80 (2.15 A˚) Asp27 (σ-σ), Arg87 (Π-Π), Arg87 (Π-Π), Ser80 (σ-σ), Ala84 (σ-σ) and binding energies ΔG =  - 6.41, - 6.97 kcal/mol with active site of topoisomerase-IV from S. pneumoniae [4KPE]. We performed a computational investigation of compounds 6a-j for their absorption, distribution, metabolism and excretion (ADME) properties by using the Molinspiration, Molsoft toolkits. The ligands 6f, 6d and 6b reveal the highest pharmacokinetic properties and possess maximum drug-likeness model score 1.59, 1.46 and 1.23, respectively.

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx-efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC₅₀) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and naïve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

Current status and future prospects of p38α/MAPK14 kinase and its inhibitors

P38α (which is also named MAPK14) plays a pivotal role in initiating different disease states such as inflammatory disorders, neurodegenerative diseases, cardiovascular cases, and cancer. Inhibitors of p38α can be utilized for treatment of these diseases. In this article, we reviewed the structural and biological characteristics of p38α, its relationship to the fore-mentioned disease states, as well as the recently reported inhibitors and classified them according to their chemical structures. We focused on the articles published in the literature during the last decade (2011-2020).

CID-6033590 inhibits p38MAPK pathway and induces S-phase cell cycle arrest and apoptosis in DU145 and PC-3 cells

Metastatic prostate cancer, with no effective treatment, is among the leading causes of cancer-associated deaths in men. Overexpression of p38αMAPK has been observed in neuroendocrine prostate cancer patients and in both DU145 and PC-3 cell lines and represents a good drug target. Sulfonamide derivatives have shown biological activities against many human diseases, including cancer. CID-6033590, a sulfonylhydrazide compound, screened from PubChem database by molecular docking with p38αMAPK, was evaluated for anti-cancerous activities. CID-6033590 induced toxicity in both DU145 and PC-3 cells in a concentration and time-dependent manner with an IC₅₀ value of 60 μM and 66 μM, respectively. Sub-cytotoxic concentrations of the compound significantly induced S-phase cell cycle arrest, inhibited cyclinA/CDK2 complex and blocked cell proliferation. Further, CID-6033590 downregulated phosphorylation of p38MAPK (P-p38) as well as its downstream targets, Activating transcription factor 2 (ATF-2) and Heat shock protein 27 (Hsp27). The compound increased ROS and decreased mitochondrial membrane potential (Δψm), downregulated Bcl-2 and survivin and cleaved poly ADP ribose polymerase (PARP) and caspase-3, indicating the induction of apoptosis. The evaluaion of the compound on noncancerous, human prostatic epithelial cell line RWPE-1, and healthy murine tissues yielded no significant toxicity. Taken together, we suggest CID-6033590 as a potential candidate for prostate cancer therapy.

Protein-ligand interaction fingerprints for accurate prediction of dissociation rates of p38 MAPK Type II inhibitors

Binding/unbinding kinetics are key determinants of drug potencies. However, there are still a lot of challenges in predicting kinetic properties during early-stage drug development. In this work, position-restrained molecular dynamics simulations combined with energy decomposition were applied to extract protein-ligand interaction (PLI) fingerprints along the unbinding pathway of 20 p38 mitogen-activated protein kinase (p38 MAPK) Type II inhibitors. The results showed that the electrostatic and/or van der Waals interaction fingerprints at three key positions can be used for accurate prediction of the dissociation rate constants (koff) of p38 MAPK Type II inhibitors. The strategy proposed in this paper can provide not only an efficient method of predicting the dissociation rates of the p38 MAPK Type II inhibitors, but also the atom-level mechanism of enthalpy-driven unbinding process.

Fragment-Based Structural Optimization of a Natural Product Itampolin A as a p38α Inhibitor for Lung Cancer

Marine animals and plants provide abundant secondary metabolites with antitumor activity. Itampolin A is a brominated natural tyrosine secondary metabolite that is isolated from the sponge Iotrochota purpurea. Recently, we have achieved the first total synthesis of this brominated tyrosine secondary metabolite, which was found to be a potent p38α inhibitor exhibiting anticancer effects. A fragment-based drug design (FBDD) was carried out to optimize itampolin A. Forty-five brominated tyrosine derivatives were synthesized with interesting biological activities. Then, a QSAR study was carried out to explore the structural determinants responsible for the activity of brominated tyrosine skeleton p38α inhibitors. The lead compound was optimized by a FBDD method, then three series of brominated tyrosine derivatives were synthesized and evaluated for their inhibitory activities against p38α and tumor cells. Compound 6o (IC₅₀ = 0.66 μM) exhibited significant antitumor activity against non-small cell lung A549 cells (A549). This also demonstrated the feasibility of the FBDD method of structural optimization.

Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery

Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.

Targeting progesterone metabolism in breast cancer with l-proline derived new 14-azasteroids

Breast cancer cell proliferation is promoted by a variety of mitogenic signals. Classically estrogen is considered as most predominant mitogenic signal in hormone-dependent breast cancer and progesterone is primarily considered to have protective effect. However, it is suggested that some progesterone metabolite may promote breast cancer and progesterone metabolites like 5α-pregnane and 4-pregnene could serve as regulators of estrogen-responsiveness of breast cancer cells. Here, we estimated the potential of alternate targeting of breast cancer via progesterone signalling. l-Proline derived novel 14-azasteroid compounds were screened against MCF-7 and MDA-MB-231 cell lines using MTT assay. In silico studies, cell cycle, Annexin-V-FITC/PI, JC-1 mitochondrial assay, ROS analysis were performed to analyse the impact of hit compound 3b on breast cancer cells. Further, we analysed the impact of hit 3b on the progesterone, its metabolites and enzymes responsible for the conversion of progesterone and its metabolites using ELISA. Data suggests that compound 3b binds and down regulates of 5α-reductase by specifically inhibiting production of progesterone metabolites that are capable of promoting breast cancer proliferation, epithelial mesenchymal transition and migration. This study establishes the proof of concept and generation of new leads for additional targeting of breast cancer.

Importance of consensus region of multiple-ligand templates in a virtual screening method

We discuss methods and ideas of virtual screening (VS) for drug discovery by examining the performance of VS-APPLE, a recently developed VS method, which extensively utilizes the tendency of single binding pockets to bind diversely different ligands, i.e. promiscuity of binding pockets. In VS-APPLE, multiple ligands bound to a pocket are spatially arranged by maximizing structural overlap of the protein while keeping their relative position and orientation with respect to the pocket surface, which are then combined into a multiple-ligand template for screening test compounds. To greatly reduce the computational cost, comparison of test compound structures are made only with limited regions of the multiple-ligand template. Even when we use the narrow regions with most densely populated atoms for the comparison, VSAPPLE outperforms other conventional VS methods in terms of Area Under the Curve (AUC) measure. This region with densely populated atoms corresponds to the consensus region among multiple ligands. It is typically observed that expansion of the sampled region including more atoms improves screening efficiency. However, for some target proteins, considering only a small consensus region is enough for the effective screening of test compounds. These results suggest that the performance test of VS methods sheds light on the mechanisms of protein-ligand interactions, and elucidation of the protein-ligand interactions should further help improvement of VS methods.

Lead identification for the K-Ras protein: virtual screening and combinatorial fragment-based approaches

OBJECTIVE

Kirsten rat sarcoma (K-Ras) protein is a member of Ras family belonging to the small guanosine triphosphatases superfamily. The members of this family share a conserved structure and biochemical properties, acting as binary molecular switches. The guanosine triphosphate-bound active K-Ras interacts with a range of effectors, resulting in the stimulation of downstream signaling pathways regulating cell proliferation, differentiation, and apoptosis. Efforts to target K-Ras have been unsuccessful until now, placing it among high-value molecules against which developing a therapy would have an enormous impact. K-Ras transduces signals when it binds to guanosine triphosphate by directly binding to downstream effector proteins, but in case of guanosine diphosphate-bound conformation, these interactions get disrupted.

METHODS

In the present study, we targeted the nucleotide-binding site in the "on" and "off" state conformations of the K-Ras protein to find out suitable lead compounds. A structure-based virtual screening approach has been used to screen compounds from different databases, followed by a combinatorial fragment-based approach to design the apposite lead for the K-Ras protein.

RESULTS

Interestingly, the designed compounds exhibit a binding preference for the "off" state over "on" state conformation of K-Ras protein. Moreover, the designed compounds' interactions are similar to guanosine diphosphate and, thus, could presumably act as a potential lead for K-Ras. The predicted drug-likeness properties of these compounds suggest that these compounds follow the Lipinski's rule of five and have tolerable absorption, distribution, metabolism, excretion and toxicity values.

CONCLUSION

Thus, through the current study, we propose targeting only "off" state conformations as a promising strategy for the design of reversible inhibitors to pharmacologically inhibit distinct conformations of K-Ras protein.

Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase

The therapeutic challenges in the treatment of tuberculosis demand multidisciplinary approaches for the identification of potential drug targets as well as fast and accurate techniques to screen huge chemical libraries. Mycobacterial cyclopropane synthase (CmaA1) has been shown to be essential for the survival of the bacteria due to its critical role in the synthesis of mycolic acids. The present study proposes pharmacophore models based on the structure of CmaA1 taking into account its various states in the cyclopropanation process, and their dynamic nature as assessed using molecular dynamics (MD) simulations. The qualities of these pharmacophore models were validated by mapping 23 molecules that have been previously reported to exhibit inhibitory activities on CmaA1. Additionally, 1398 compounds that have been shown to be inactive for tuberculosis were collected from the ChEMBL database and were screened against the models for validation. The models were further validated by comparing the results from pharmacophore mapping with the results obtained from docking these molecules with the respective protein structures. The best models are suggested by validating all the models based on their screening abilities and by comparing with docking results. The models generated from the MD trajectories were found to perform better than the one generated based on the crystal structure demonstrating the importance of incorporating receptor flexibility in drug design.

Specificity rendering 'hot-spots' for aurora kinase inhibitor design: the role of non-covalent interactions and conformational transitions

The present study examines the conformational transitions occurring among the major structural motifs of Aurora kinase (AK) concomitant with the DFG-flip and deciphers the role of non-covalent interactions in rendering specificity. Multiple sequence alignment, docking and structural analysis of a repertoire of 56 crystal structures of AK from Protein Data Bank (PDB) has been carried out. The crystal structures were systematically categorized based on the conformational disposition of the DFG-loop [in (DI) 42, out (DO) 5 and out-up (DOU) 9], G-loop [extended (GE) 53 and folded (GF) 3] and αC-helix [in (CI) 42 and out (CO) 14]. The overlapping subsets on categorization show the inter-dependency among structural motifs. Therefore, the four distinct possibilities a) 2W1C (DI, CI, GE) b) 3E5A (DI, CI, GF) c) 3DJ6 (DI, CO, GF) d) 3UNZ (DOU, CO, GF) along with their co-crystals and apo-forms were subjected to molecular dynamics simulations of 40 ns each to evaluate the variations of individual residues and their impact on forming interactions. The non-covalent interactions formed by the 157 AK co-crystals with different regions of the binding site were initially studied with the docked complexes and structure interaction fingerprints. The frequency of the most prominent interactions was gauged in the AK inhibitors from PDB and the four representative conformations during 40 ns. Based on this study, seven major non-covalent interactions and their complementary sites in AK capable of rendering specificity have been prioritized for the design of different classes of inhibitors.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.