Aroma WaterLOGSY: a fast and sensitive screening tool for drug discovery

One-dimensional NMR spectroscopy has proven to be a powerful technique for screening compound libraries in drug discovery. We report a novel water ligand-observed gradient spectroscopy (WaterLOGSY) pulse sequence, named Aroma WaterLOGSY, that selectively detects aromatic WaterLOGSY signals from compounds or ligands. In the Aroma WaterLOGSY, water magnetization is untouched after water excitation and utilizes the whole period of the remaining pulse sequence to relax back to the +z direction. Due to the phase cycling design, the water magnetization is allowed to relax for the period of two full scans before it gets inverted again. Therefore, the recycle delay can be significantly shortened. Within similar experimental time, Aroma WaterLOGSY shows approximately two times higher sensitivity than the standard scheme. This method also allows the use of non-deuterated reagents, thereby accelerating experimental set-up time for ligand-binding studies.     

Integrating virtual screening and combinatorial chemistry for accelerated drug discovery

Virtual screening is increasingly being used in drug discovery programs with a growing number of successful applications. Experimental methodologies developed to speed up the drug discovery processes include high-throughput screening and combinatorial chemistry. The complementarities between computational and experimental screenings have been recognized and reviewed in the literature. Computational methods have also been used in the combinatorial chemistry field, in particular in library design. However, the integration of computational and combinatorial chemistry screenings has been attempted only recently. Combinatorial libraries (experimental or virtual) represent a notable source of chemically related compounds. Advances in combinatorial chemistry and deconvolution strategies, have enabled the rapid exploration of novel and dense regions in the chemical space. The present review is focused on the integration of virtual and experimental screening of combinatorial libraries. Applications of virtual screening to discover novel anticancer agents and our ongoing efforts towards the integration of virtual screening and combinatorial chemistry are also discussed.     

HAD: an automated database tool for analyzing screening hits in drug discovery

Collecting, organizing, and reviewing chemical information associated with screening hits are human time-consuming. The task depends highly on the individual, and human errors may result in missing leads or wasting resources. To overcome these hurdles, we have developed a decision support system, Hits Analysis Database (HAD). HAD is a software tool that automatically generates an ISIS database file containing compound structures, biological activities, calculated properties such as clogP, hazard fragment labels, structure classifications, etc. All data are processed by available software and packed into a single SD file. In addition to search capabilities, HAD provides an overview of structural classes and associated activity statistics. Chemical structures can be organized by maximum common substructure clustering. The ease of use and customized features make HAD a chief tool in lead selection processes.     

In silico fragment-mapping method: a new tool for fragment-based/structure-based drug discovery

Here, we propose an in silico fragment-mapping method as a potential tool for fragment-based/structure-based drug discovery (FBDD/SBDD). For this method, we created a database named Canonical Subsite–Fragment DataBase (CSFDB) and developed a knowledge-based fragment-mapping program, Fsubsite. CSFDB consists of various pairs of subsite–fragments derived from X-ray crystal structures of known protein–ligand complexes. Using three-dimensional similarity-matching between subsites on one protein and another, Fsubsite compares the surface of a target protein with all subsites in CSFDB. When a local topography similar to the subsite is found on the surface, Fsubsite places a fragment combined with the subsite in CSFDB on the target protein. For validation purposes, we applied the method to the apo-structure of cyclin-dependent kinase 2 (CDK2) and identified four compounds containing three mapped fragments that existed in the list of known inhibitors of CDK2. Next, the utility of our fragment-mapping method for fragment-growing was examined on the complex structure of tRNA-guanine transglycosylase with a small ligand. Fsubsite mapped appropriate fragments on the same position as the binding ligand or in the vicinity of the ligand. Finally, a 3D-pharmacophore model was constructed from the fragments mapped on the apo-structure of heat shock protein 90-α (HSP90α). Then, 3D pharmacophore-based virtual screening was carried out using a commercially available compound database. The resultant hit compounds were very similar to a known ligand of HSP90α. As a result of these findings, this in silico fragment-mapping method seems to be a useful tool for computational FBDD and SBDD.     

Fragment-based lead discovery: challenges and opportunities

Fragment-based lead discovery has undergone remarkable changes over the last 15 years. During this time, the pharmaceutical industry has changed dramatically as well, and continued evolution of the industry is assured. These changes present many challenges but also several opportunities for executing fragment-based drug design. This article will explore some of the more significant changes in the industry and how they may affect future discovery efforts related to fragment-based initiatives.     

Chromatography: An important tool for drug discovery

Despite substantial developments of extraction and separation techniques, isolation of natural products from natural sources is still a challenging task. Undoubtedly hybrid methods like liquid chromatography with NMR spectroscopy or liquid chromatography coupled with mass spectrometry made on‐line structure elucidation possible and provided impressive examples of natural product identification without prior isolation, however, in many cases the necessity to get the purified compounds in hand is still a fact. The process begins with the collection of desired plant material which is subjected to the suitable extraction process. The complex crude extracts are then monitored by various chromatographic procedures to separate and quantify the desired compounds. The active plant extracts are then fractionated to isolate the bioactive compounds in their pure form. The fully identified compound is used as a lead for the production of related analogues to modulate the biological activity and to carry out structure‐activity relationship. The major isolated bioactive compound is used for semi‐synthetic modification or total synthesis should be carried out such that it is relatively easy to modify the structure of the lead compound. This is a simple and cost‐effective way to increase the chance to discover lead compounds. The biological activity in vitro and in vivo has to be done after purification.     

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.     

StructRank: a new approach for ligand-based virtual screening

Screening large libraries of chemical compounds against a biological target, typically a receptor or an enzyme, is a crucial step in the process of drug discovery. Virtual screening (VS) can be seen as a ranking problem which prefers as many actives as possible at the top of the ranking. As a standard, current Quantitative Structure-Activity Relationship (QSAR) models apply regression methods to predict the level of activity for each molecule and then sort them to establish the ranking. In this paper, we propose a top-k ranking algorithm (StructRank) based on Support Vector Machines to solve the early recognition problem directly. Empirically, we show that our ranking approach outperforms not only regression methods but another ranking approach recently proposed for QSAR ranking, RankSVM, in terms of actives found.     

RAMPED-UP NMR: multiplexed NMR-based screening for drug discovery

The crucial step in drug discovery is the identification of a lead compound from a vast chemical library by any number of screening techniques. NMR-based screening has the advantage of directly detecting binding of a compound to the target. The spectra resulting from these screens can also be very complex and difficult to analyze, making this an inefficient process. We present here a method, RAMPED-UP NMR, (Rapid Analysis and Multiplexing of Experimentally Discriminated Uniquely Labeled Proteins using NMR) which generates simple spectra which are easy to interpret and allows several proteins to be screened simultaneously. In this method, the proteins to be screened are uniquely labeled with one amino acid type. There are several benefits derived from this unique labeling strategy: the spectra are greatly simplified, resonances that are most likely to be affected by binding are the only ones observed, and peaks that yield little or no information upon binding are eliminated, allowing the analysis of multiple proteins easily and simultaneously. We demonstrate the ability of three different proteins to be analyzed simultaneously for binding to two different ligands. This method will have significant impact in the use of NMR spectroscopy for both the lead generation and lead optimization phases of drug discovery by its ability to increase screening throughput and the ability to examine selectivity. To the best of our knowledge, this is the first time in any format that multiple proteins can be screened in one tube.     

Combinatorial chemistry: a tool for the discovery of new catalysts

The discovery of new reactions and catalysts has always presented an intriguing challenge to scientists. With the rise of combinatorial chemistry, a new method has emerged that holds considerable promise to facilitate the task since it allows for the simultaneous generation and testing of a large number of compounds. The crucial difficulty lies in establishing general technologies for rapid and reliable screening of libraries to determine the catalytic activity of their members. Several recent publications have addressed this question by using infrared thermography, colorimetric assays and fluorescence spectroscopy. These techniques have not only been applied successfully to the high-throughput screening of parallel compound arrays but also to the screening of one-bead-one-compound libraries. This demonstrates that combinatorial chemistry possesses indeed the potential to establish itself as a powerful tool for the discovery of new catalysts. This review describes the methodologies used so far for the detection of catalytic events and will place particular emphasis on the on-bead screening of one-bead-one-compound libraries.     

Aqueous microwave chemistry: a clean and green synthetic tool for rapid drug discovery

The development of "Greener Organic Chemistry" is due to the recognition that environmentally friendly products and processes will be economical in the long term as they circumvent the need for treating 'end-of-the-pipe' pollutants and by-products generated by conventional synthesis. The fundamentals and significant outcomes of microwave-assisted organic synthesis in aqueous medium are summarized in this tutorial review, which have resulted in the development of relatively sustainable and environmentally benign protocols for the synthesis of drugs and fine chemicals.     

Peptide phage display as a tool for drug discovery: targeting membrane receptors

Ligands selected from phage-displayed random peptide libraries tend to be directed to biologically relevant sites on the surface of the target protein. Consequently, peptides derived from library screenings often modulate the target protein's activity in vitro and in vivo and can be used as lead compounds in drug design and as alternatives to antibodies for target validation in both genomics and drug discovery. This review discusses the use of phage display to identify membrane receptor modulators with agonistic or antagonistic activities. Because isolating or producing recombinant membrane proteins for use as target molecules in library screening is often impossible, innovative selection strategies such as panning against whole cells or tissues, recombinant receptor ectodomains, or neutralizing antibodies to endogenous binding partners were devised. Prominent examples from a two-decade history of peptide phage display will be presented, focusing on the design of affinity selection experiments, methods for improving the initial hits, and applications of the identified peptides.     

Cytokine antibody arrays: a promising tool to identify molecular targets for drug discovery

Cytokines play important roles in normal cell functions and changes in cytokines have been implicated in many diseases. Recent efforts have focused on developing cytokine antibody arrays. These arrays allow investigators to simultaneously detect multiple cytokines in qualitative and quantitative ways. Cytokine antibody array systems feature high sensitivity, specificity and throughput. This novel technology opens up an expanding spectrum of applications in drug discovery, including target discovery, target validation, screening for lead compounds, compound optimization and clinical trials.     

MED-SuMoLig: a new ligand-based screening tool for efficient scaffold hopping

The identification of small molecules with selective bioactivity, whether intended as potential therapeutics or as tools for experimental research, is central to progress in medicine and in the life sciences. To facilitate such study, we have developed a ligand-based program well-suited for effective screening of large compound collections. This package, MED-SuMoLig, combines a SMARTS-driven substructure search aiming at 3D pharmacophore profiling and computation of the local atomic density of the compared molecules. The screening utility was then investigated using 52 diverse active molecules (against CDK2, Factor Xa, HIV-1 protease, neuraminidase, ribonuclease A, and thymidine kinase) merged to a library of about 40,000 putative inactive (druglike) compounds. In all cases, the program recovered more than half of the actives in the top 3% of the screened library. We also compared the performance of MED-SuMoLig with that of ChemMine or of ROCS and found that MED-SuMoLig outperformed both methods for CDK2 and Factor Xa in terms of enrichment rates or performed equally well for the other targets.     

Congestion game scheduling for virtual drug screening optimization

In virtual drug screening, the chemical diversity of hits is an important factor, along with their predicted activity. Moreover, interim results are of interest for directing the further research, and their diversity is also desirable. In this paper, we consider a problem of obtaining a diverse set of virtual screening hits in a short time. To this end, we propose a mathematical model of task scheduling for virtual drug screening in high-performance computational systems as a congestion game between computational nodes to find the equilibrium solutions for best balancing the number of interim hits with their chemical diversity. The model considers the heterogeneous environment with workload uncertainty, processing time uncertainty, and limited knowledge about the input dataset structure. We perform computational experiments and evaluate the performance of the developed approach considering organic molecules database GDB-9. The used set of molecules is rich enough to demonstrate the feasibility and practicability of proposed solutions. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hit discovery rate and chemical diversity at earlier steps. Based on these results, we use a social utility metric for assessing the efficiency of our equilibrium solutions and show that they reach greatest values.     

Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.