Virtual Screening, Molecular Docking and QSAR Studies in Drug Discovery and Development Programme

Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two basic approaches of computer-aided drug design (CADD) used in modern drug discovery and development programme. Virtual screening (or in silico screening) has been used in drug discovery program as a complementary tool to high throughput screening (HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained considerable interest in the pharmaceutical research as a productive and cost-effective technology in search for novel molecules of medicinal interest. Docking is also used for virtual screening of new ligands on the basis of biological structures for identification of hits and generation of leads or optimization (potency/ property) of leads in drug discovery program. Hence, docking is approach of SBDD which plays an important role in rational designing of new drug molecules. Quantitative structure-activity relationship (QSAR) is an important chemometric tool in computational drug design. It is a common practice of LBDD. The study of QSAR gives information related to structural features and/or physicochemical properties of structurally similar molecules to their biological activity. In this paper, a comprehensive review on several computational tools of SBDD and LBDD such as virtual screening, molecular docking and QSAR methods of and their applications in the drug discovery and development programme have been summarized. Keywords: Virtual screening, Molecular docking, QSAR, Drug discovery, Lead molecul

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Controlling platinum, ruthenium, and osmium reactivity for anticancer drug design

The main task of the medicinal chemist is to design molecules that interact specifically with derailed or degenerating processes in a diseased organism, translating the available knowledge of pathobiochemical and physiological data into chemically useful information and structures. Current knowledge of the biological and chemical processes underlying diseases is vast and rapidly expanding. In particular the unraveling of the genome in combination with, for instance, the rapid development of structural biology has led to an explosion in available information and identification of new targets for chemotherapy. The task of translating this wealth of data into active and selective new drugs is an enormous, but realistic, challenge. It requires knowledge from many different fields, including molecular biology, chemistry, pharmacology, physiology, and medicine and as such requires a truly interdisciplinary approach. Ultimately, the goal is to design molecules that satisfy all the requirements for a candidate drug to function therapeutically. Therapeutic activity can then be achieved by an understanding of and control over structure and reactivity of the candidate drug through molecular manipulation

Molecular Modeling Studies of Curcumin Analogs as Anti-Angiogenic Agents

Angiogenesis plays a pivotal role in the metastasis of cancer: curcumin showed excellent anti-angiogenesis activity on metastatic tumors. Several curcumin analogues have been synthesized and studied, and their biological activity was reported in the literature. One class of potent analogues are aromatic enones. In Dr Bowen's laboratory sixty three compounds were synthesized and in the laboratory of Dr Jack Arbizer (Emory University, Atlanta, GA) they were tested for their anti-angiogenic activity with an SVR endothelial cell growth assay developed by Dr Arbizer. The precise mechanism or the specific biological target on which these analogs exert their inhibition potential as anti-angiogenic agents is unknown. Therefore, structure-based molecular modeling is not a possibility. However, ligand based molecular modeling methods are available for studying and predicting which compounds among the sixty three can be further optimized for selectivity and desired property. Computational studies were carried out to identify which structural features within the series of analogues are significantly important for activity. Initially, pharmacophore modeling was carried out in Molecular Operating Environment (MOE) software to identify the Interaction Pharmacophore Elements (IPE) and their relative geometry in three-dimensional space. Two different three dimensional quantitative structural Activity Relationship (3D-QSAR) studies, Comparative Molecular Field Analysis (CoMFA), and Comparative Molecular Similarity Indices Analysis (CoMSIA) were carried out with this dataset. SYBYL (versions 7.2 and 7.3) were used for the development of the models. Forty six compounds were used as the calibration or the training set. The model yielded a cross validated q2 of 0.289 for CoMFA and 0.146 for CoMSIA analyses. Eleven compounds were used as the test set (or the prediction) set to externally validate the QSAR models and their robustness. The predictions of the model are acceptable with a few outliers

Databases and QSAR for Cancer Research

In this review, we take a survey of bioinformatics databases and quantitative structure-activity relationship studies reported in published literature. Databases from the most general to special cancer-related ones have been included. Most commonly used methods of structure-based analysis of molecules have been reviewed, along with some case studies where they have been used in cancer research. This article is expected to be of use for general bioinformatics researchers interested in cancer and will also provide an update to those who have been actively pursuing this field of research

Modelling the anti-methicillin-resistant staphylococcus aureus (MRSA) activity of cannabinoids: a QSAR and Docking study

Twenty-four cannabinoids active against MRSA SA1199B and XU212 were optimized at WB97XD/6-31G(d,p), and several molecular descriptors were obtained. Using a multiple linear regression method, several mathematical models with statistical significance were obtained. The robustness of the models was validated, employing the leave-one-out cross-validation and Y-scrambling methods. The entire data set was docked against penicillin-binding protein, iso-tyrosyl tRNA synthetase, and DNA gyrase. The most active cannabinoids had high affinity to penicillin-binding protein (PBP), whereas the least active compounds had low affinities for all of the targets. Among the cannabinoid compounds, Cannabinoid 2 was highlighted due to its suitable combination of both antimicrobial activity and higher scoring values against the selected target; therefore, its docking performance was compared to that of oxacillin, a commercial PBP inhibitor. The 2D figures reveal that both compounds hit the protein in the active site with a similar type of molecular interaction, where the hydroxyl groups in the aromatic ring of cannabinoids play a pivotal role in the biological activity. These results provide some evidence that the anti-Staphylococcus aureus activity of these cannabinoids may be related to the inhibition of the PBP protein; besides, the robustness of the models along with the docking and Quantitative Structure–Activity Relationship (QSAR) results allow the proposal of three new compounds; the predicted activity combined with the scoring values against PBP should encourage future synthesis and experimental testing

IDENTIFICATION OF POTENTIAL NOVEL EGFR INHIBITORS USING A COMBINATION OF PHARMACOPHORE AND DOCKING METHODS

Objective: Identifying new inhibitors of Epidermal Growth Factor Receptor (EGFR) by virtual screening using a pharmacophore model followed by docking. Methods: A pharmacophore model was developed using a dataset of 77 chemically diverse EGFR inhibitors using PHASE. Statistically valid Three Dimensional Quantitative Structure Activity Relationship (3D-QSAR) equations were generated for the pharmacophore model. This was followed by database screening to obtain probable hits. Docking of the probable hits into the crystal structure of EGFR was used as a second filter. Docking studies were carried out using GLIDE. Calculation of ADME properties of the probable hits arising out of docking further reduced the number of hits. Results: A five-point pharmacophore was generated for EGFR inhibitors reported in literature. The pharmacophore indicated that the presence of two aromatic ring features (R), one acceptor feature (A), one donor feature (D) and one hydrophobic feature (H) is necessary for potent inhibitory activity. The generated pharmacophore yielded statistically significant 3D-QSAR model, with a correlation coefficient r2 of 0.9905 and q2 of 0.8764. Virtual screening using the best pharmacophore model resulted in 372 hits. Docking studies as a second filter reduced the hits to 8. Application of drug-likeness as a third filter gave 6 leads.Conclusion: 6 leads with satisfactory pharmacokinetics properties were identified as potential EGFR inhibitors. This study may facilitate development of some new potential EGFR inhibitors. Â

Ligand based pharmacophore modelling of anticancer histone deacetylase inhibitors

Histone deacetylases have emerged as an important therapeutic target for the treatment of cancer. Genome-wide histone hypoacetylation causes many cancers. Recently, inhibitors of histone deacetylases (HDAC) have emerged as an important class of anticancer agents. Various side effectslike myocardium damage and bone marrow depression even leading to cell death have been observed in the treatment of caner cells using HDAC inhibitors. The discovery and development of type-specific HDAC inhibitors is of both research and clinical interests. Ligand based pharmacophore modelling is playing a key role for the identification of ligand features for the particular targets. We present a model for designing the pharmacophore onto the set of 70 compounds of three different classes and two subclasses. The ligand based pharmacophore model has been identified in order to facilitate the discovery of type specific anticancer HDAC inhibitors. The result indicates that the in silico methodsare useful in predicting the biological activity of the compound or compound library by screening it against a predicted pharmacophore. Ligand Scout 2.02 has been used to predict the pharmacophorefeatures for anticancer HDAC inhibitors and the distances between pharmacophore features have been calculated through the software Jmol. The proposed model has been validated by docking the MS275compound into the binding pocket of Human HDAC8. Our discovery will help in the identification of more specific anticancer human HDAC inhibitors

Prediction of Inhibitory Activity of Epidermal Growth Factor Receptor Inhibitors Using Grid Search-Projection Pursuit Regression Method

The epidermal growth factor receptor (EGFR) protein tyrosine kinase (PTK) is an important protein target for anti-tumor drug discovery. To identify potential EGFR inhibitors, we conducted a quantitative structure–activity relationship (QSAR) study on the inhibitory activity of a series of quinazoline derivatives against EGFR tyrosine kinase. Two 2D-QSAR models were developed based on the best multi-linear regression (BMLR) and grid-search assisted projection pursuit regression (GS-PPR) methods. The results demonstrate that the inhibitory activity of quinazoline derivatives is strongly correlated with their polarizability, activation energy, mass distribution, connectivity, and branching information. Although the present investigation focused on EGFR, the approach provides a general avenue in the structure-based drug development of different protein receptor inhibitors