Prospective performance evaluation of selected common virtual screening tools. Case study: Cyclooxygenase (COX) 1 and 2

Plant Metabolites as SARS-CoV-2 Inhibitors Candidates: In Silico and In Vitro Studies

Since it acquired pandemic status, SARS-CoV-2 has been causing all kinds of damage all over the world. More than 6.3 million people have died, and many cases of sequelae are in survivors. Currently, the only products available to most of the world’s population to fight the pandemic are vaccines, which still need improvement since the number of new cases, admissions into intensive care units, and deaths are again reaching worrying rates, which makes it essential to compounds that can be used during infection, reducing the impacts of the disease. Plant metabolites are recognized sources of diverse biological activities and are the safest way to research anti-SARS-CoV-2 compounds. The present study computationally evaluated 55 plant compounds in five SARS-CoV-2 targets such Main Protease (Mpro or 3CL or MainPro), RNA-dependent RNA polymerase (RdRp), Papain-Like Protease (PLpro), NSP15 Endoribonuclease, Spike Protein (Protein S or Spro) and human Angiotensin-converting enzyme 2 (ACE-2) followed by in vitro evaluation of their potential for the inhibition of the interaction of the SARS-CoV-2 Spro with human ACE-2. The in silico results indicated that, in general, amentoflavone, 7-O-galloylquercetin, kaempferitrin, and gallagic acid were the compounds with the strongest electronic interaction parameters with the selected targets. Through the data obtained, we can demonstrate that although the indication of individual interaction of plant metabolites with both Spro and ACE-2, the metabolites evaluated were not able to inhibit the interaction between these two structures in the in vitro test. Despite this, these molecules still must be considered in the research of therapeutic agents for treatment of patients affected by COVID-19 since the activity on other targets and influence on the dynamics of viral infection during the interaction Spro x ACE-2 should be investigated.

A primer on natural product-based virtual screening

Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Herbonanoceuticals: A Novel Beginning in Drug Discovery and Therapeutics

The Indian pharmaceutical industry is the world’s second largest industry (by volume) that develops products and market drugs licensed for use as medications. Medicines manufactured in the modern era are associated with major controversies such as non–target specificity, resistance, repeated administration, immune rejection, and other adverse effects on the body. Thus, there is a great need to find drugs that do not raise the aforementioned issues. Nature is an excellent hub providing a diverse range of phytoconstituents that open the way to phototherapeutics, which need a scientific path to deliver the active elements in a supported way to increase patient compliance and reduce the need for repeated administration. To discover a novel phytochemical as a lead compound for a therapeutic purpose is a real challenge. In former times, drug discovery was a complex process, as it took several years to find a lead compound for use against a particular disease. Nowadays, however, virtual screening methods have been developed, which are target specific, time consuming, and cost effective. To avoid increased and repeated administration of a drug, nanosized drug delivery systems for herbal drugs have been developed to enhance the activity and overcome problems associated with synthetic medicines. This review summarizes three main fields: drug discovery, docking for drug design, and last—but not least—drug delivery systems. Nowadays, nanobased drug delivery systems are in demand for delivery of herbal medicines used for therapeutic purposes. Herbonanoceuticals—herbal drugs of a nanosize—have better remedial value and fewer detrimental effects than modern medicines. Therefore, herbonanoceuticals can be a boon in the field of therapeutics.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Design and synthesis of some new 2,3′-bipyridine-5-carbonitriles as potential anti-inflammatory/antimicrobial agents

Aim: Inflammation may cause accumulation of fluid in the injured area, which may promote bacterial growth. Other reports disclosed that non-steroidal anti-inflammatory drugs may enhance progression of bacterial infection. Results: This work describes synthesis of new series of 2,3′-bipyridine-5-carbonitriles as structural analogs of etoricoxib, linked at position-6 to variously substituted thio or oxo moieties. Biological screening results revealed that compounds 2b, 4b, 7e and 8 showed significant acute and chronic AI activities and broad spectrum of antimicrobial activity. In addition, similarity ensemble approach was applied to predict potential biological targets of the tested compounds. Then, pharmacophore modeling study was employed to determine the most important structural parameters controlling bioactivity. Moreover, title compounds showed physicochemical properties within those considered adequate for drug candidates. Conclusion: This study explored the potential of such series of compounds as structural leads for further modification to develop a new class of dual AI-antimicrobial agents.

Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening

Autodock Vina is a very popular, and highly cited, open source docking program. Here we present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets. We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities. On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance. This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications. Vinardo outperforms Vina in all tests performed, for all datasets analyzed. The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v2.0 from http://smina.sf.net. Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address.

Optimized Virtual Screening Workflow for the Identification of Novel G-Quadruplex Ligands

G-quadruplexes, alternative DNA secondary structures present in telomeres, emerge as promising targets for the treatment of cancer, because they prevent telomere elongation and accordingly cell proliferation. Within this study, theoretically validated pharmacophore- and shape-based models as well as a theoretically validated docking protocol were generated and applied in parallel for virtual screening and the identification of novel G-quadruplex ligands. Top-ranked hits retrieved with all methods independently and in addition in a consensus approach were selected for biological testing. Of the 32 tested virtual hits seven selectively stabilized G-quadruplexes over duplex DNA in the fluorescence melting assay. For the five most active compounds, chemically closely related analogues were collected and subjected to in vitro analysis. Thereby, seven further novel G-quadruplex ligands could be identified. These molecules do not only represent novel scaffolds, but some of them are in addition even more potent G-quadruplex stabilizers than the established reference compound berberine. This study proposes an optimized in silico workflow for the identification of novel G-quadruplex stabilizers, which can also be applied in future studies. In addition, structurally novel and promising lead candidates with strong and selective G-quadruplex stabilizing properties are reported.