Large Chemical Space Research Articles

BioSM: Metabolomics Tool for Identifying Endogenous Mammalian Biochemical Structures in Chemical Structure Space

The structural identification of unknown biochemical compounds in complex biofluids continues to be a major challenge in metabolomics research. Using LC/MS, there are currently two major options for solving this problem: searching small biochemical databases, which often do not contain the unknown of interest or searching large chemical databases which include large numbers of nonbiochemical compounds. Searching larger chemical databases (larger chemical space) increases the odds of identifying an unknown biochemical compound, but only if nonbiochemical structures can be eliminated from consideration. In this paper we present BioSM; a cheminformatics tool that uses known endogenous mammalian biochemical compounds (as scaffolds) and graph matching methods to identify endogenous mammalian biochemical structures in chemical structure space. The results of a comprehensive set of empirical experiments suggest that BioSM identifies endogenous mammalian biochemical structures with high accuracy. In a leave-one-out cross validation experiment, BioSM correctly predicted 95% of 1388 Kyoto Encyclopedia of Genes and Genomes (KEGG) compounds as endogenous mammalian biochemicals using 1565 scaffolds. Analysis of two additional biological data sets containing 2330 human metabolites (HMDB) and 2416 plant secondary metabolites (KEGG) resulted in biochemical annotations of 89% and 72% of the compounds, respectively. When a data set of 3895 drugs (DrugBank and USAN) was tested, 48% of these structures were predicted to be biochemical. However, when a set of synthetic chemical compounds (Chembridge and Chemsynthesis databases) were examined, only 29% of the 458,207 structures were predicted to be biochemical. Moreover, BioSM predicted that 34% of 883,199 randomly selected compounds from PubChem were biochemical. We then expanded the scaffold list to 3927 biochemical compounds and reevaluated the above data sets to determine whether scaffold number influenced model performance. Although there were significant improvements in model sensitivity and specificity using the larger scaffold list, the data set comparison results were very similar. These results suggest that additional biochemical scaffolds will not further improve our representation of biochemical structure space and that the model is reasonably robust. BioSM provides a qualitative (yes/no) and quantitative (ranking) method for endogenous mammalian biochemical annotation of chemical space and, thus, will be useful in the identification of unknown biochemical structures in metabolomics. BioSM is freely available at http://metabolomics.pharm.uconn.edu.

High-Throughput Development of Amphiphile Self-Assembly Materials: Fast-Tracking Synthesis, Characterization, Formulation, Application, and Understanding

Amphiphile self-assembly materials, which contain both a hydrophilic and a hydrophobic domain, have great potential in high-throughput and combinatorial approaches to discovery and development. However, the materials chemistry community has not embraced these ideas to anywhere near the extent that the medicinal chemistry community has. While this situation is beginning to change, extracting the full potential of high-throughput approaches in the development of self-assembling materials will require further development in the synthesis, characterization, formulation, and application domains. One of the key factors that make small molecule amphiphiles prospective building blocks for next generation multifunctional materials is their ability to self-assemble into complex nanostructures through low-energy transformations. Scientists can potentially tune, control, and functionalize these structures, but only after establishing their inherent properties. Because both robotic materials handling and customized rapid characterization equipment are increasingly available, high-throughput solutions are now attainable. These address traditional development bottlenecks associated with self-assembling amphiphile materials, such as their structural characterization and the assessment of end-use functional performance. A high-throughput methodology can help streamline materials development workflows, in accord with existing high-throughput discovery pipelines such as those used by the pharmaceutical industry in drug discovery. Chemists have identified several areas that are amenable to a high-throughput approach for amphiphile self-assembly materials development. These allow an exploration of not only a large potential chemical, compositional, and structural space, but also material properties, formulation, and application variables. These areas of development include materials synthesis and preparation, formulation, characterization, and screening performance for the desired end application. High-throughput data analysis is crucial at all stages to keep pace with data collection. In this Account, we describe high-throughput advances in the field of amphiphile self-assembly, focusing on nanostructured lyotropic liquid crystalline materials, which form when amphiphiles are added to a polar solvent. We outline recent progress in the automated preparation of amphiphile molecules and their nanostructured self-assembly systems both in the bulk phase and in dispersed colloidal particulate systems. Once prepared, we can structurally characterize these systems by establishing phase behavior in a high-throughput manner with both laboratory (infrared and light polarization microscopy) and synchrotron facilities (small-angle X-ray scattering). Additionally, we provide three case studies to demonstrate how chemists can use high-throughput approaches to evaluate the functional performance of amphiphile self-assembly materials. The high-throughput methodology for the set-up and characterization of large matrix in meso membrane protein crystallization trials can illustrate an application of bulk phase self-assembling amphiphiles. For dispersed colloidal systems, two nanomedicine examples highlight advances in high-throughput preparation, characterization, and evaluation: drug delivery and magnetic resonance imaging agents.

Discovery of Potent, Selective Multidrug and Toxin Extrusion Transporter 1 (MATE1, SLC47A1) Inhibitors Through Prescription Drug Profiling and Computational Modeling

The human multidrug and toxin extrusion (MATE) transporter 1 contributes to the tissue distribution and excretion of many drugs. Inhibition of MATE1 may result in potential drug-drug interactions (DDIs) and alterations in drug exposure and accumulation in various tissues. The primary goals of this project were to identify MATE1 inhibitors with clinical importance or in vitro utility and to elucidate the physicochemical properties that differ between MATE1 and OCT2 inhibitors. Using a fluorescence assay of ASP(+) uptake in cells stably expressing MATE1, over 900 prescription drugs were screened and 84 potential MATE1 inhibitors were found. We identified several MATE1 selective inhibitors including four FDA-approved medications that may be clinically relevant MATE1 inhibitors and could cause a clinical DDI. In parallel, a QSAR model identified distinct molecular properties of MATE1 versus OCT2 inhibitors and was used to screen the DrugBank in silico library for new hits in a larger chemical space.

Higher-order multicomponent reactions: beyond four reactants

Multicomponent reactions (MCRs) are by far the most successful class of reactions leading to high structural diversity and molecular complexity through a single transformation. As part of the ongoing search for pharmacologically active lead structures, the obtained structural diversity allows for the fast exploration of a large chemical space. Not surprisingly, the development of MCRs, leading to new structural frameworks or serving as key transformations in the total synthesis of natural products, has expanded rapidly over the last few decades. To date a multitude of new three- and four-component reactions have already been described; however, examples of "higher-order" MCRs where five or even more components are combined in a single reaction vessel are remarkably scarce. This tutorial review aims to critically describe the developments achieved in recent years, charting the ideas, challenges, and milestone reactions that were essential for the progress of this field.

Various cyclization scaffolds by a truly Ugi 4-CR

Efficient access to a large chemical space based on new scaffolds with defined 3D conformations and highly variable in the side chains is needed to find novel functional materials. Four heterocyclic scaffolds based on a four component Ugi reaction of α-amino acids, oxo components, isocyanides and primary or secondary amines suitably functionalized are described. A handful of examples are described for each scaffold.

Advances in chromatography: Efficient profiling of crude extracts and isolation of natural products

The large chemical space occupied by natural products (NPs) is directly linked to a high variability of their intrinsic physicochemical properties that render their separation, detection and characterization challenging. The analysis of NPs in complex crude extracts requires efficient separation methods. In this respect, high-performance liquid chromatography (HPLC) has been recognized since the early 1980s as the most versatile technique for their profiling directly in crude mixtures without the need for complex sample preparation.[1] HPLC has greatly developed through the years in terms of convenience, speed, choice of column stationary phases, high sensitivity, applicability to a broad variety of sample matrices, and ability to hyphenate the chromatographic methods to spectroscopic detectors. The latest developments of HPLC, including the recent introduction of very pH-stable phases, sub-2-µm particles, monolith and fused core columns, have considerably improved the performances of HPLC. In particular in phytochemical analysis, the recent introduction of Ultra High Pressure Liquid Chromatography (UHPLC) systems operating at very high pressures and using sub-2µm packing columns have allowed a remarkable decrease in analysis time and increase in peak capacity, sensitivity and reproducibility compared to conventional HPLC.[2] Such developments have a significant impact on the quality of data that can be obtained for metabolite profiling, dereplication studies and metabolomics when efficient spectroscopic detectors such as time-of-flight mass spectrometers (TOF)-MS are hyphenated to UHPLC. On the other hand, a good mastery of the chromatography parameters at the analytical scale enables efficient gradient transfer to the semi-preparative or the preparative scale based on chromatographic calculation. This efficient upscale allows high loading of crude mixture either for rapid at-line biological profiling of crude extracts with sensitive assay and/or de novo structure determination with microflow NMR methods with microgram of NPs. These developments, as well as other new trends in chromatography such as the renewed interest for superfluid critical fluid separation, the use of two dimensional LC or the hyphenation with additional separation efficiency in the gas phase provided by ion mobility in LC-MS hyphenation for the deconvolution of NPs in complex crude plant extracts will be discussed.

HiTSEE KNIME: a visualization tool for hit selection and analysis in high-throughput screening experiments for the KNIME platform

We present HiTSEE (High-Throughput Screening Exploration Environment), a visualization tool for the analysis of large chemical screens used to examine biochemical processes. The tool supports the investigation of structure-activity relationships (SAR analysis) and, through a flexible interaction mechanism, the navigation of large chemical spaces. Our approach is based on the projection of one or a few molecules of interest and the expansion around their neighborhood and allows for the exploration of large chemical libraries without the need to create an all encompassing overview of the whole library. We describe the requirements we collected during our collaboration with biologists and chemists, the design rationale behind the tool, and two case studies on different datasets. The described integration (HiTSEE KNIME) into the KNIME platform allows additional flexibility in adopting our approach to a wide range of different biochemical problems and enables other research groups to use HiTSEE.

Chemical and Biological Properties of Frequent Screening Hits

High-throughput screening (HTS) has become an important technology for the drug discovery process. It has been noted that certain compounds frequently appear as hits in many screening campaigns. By mining an HTS database covering large chemical space and diverse biological functions, we identified many novel chemical features, as well as several biological processes that were associated with a significant portion of frequent hits. However, we also noted that several marketed drugs also contained characteristics that commonly were associated with frequent hits. This observation suggested that current generally employed strategies for triaging compounds may result in the removal of compounds with desirable properties. Therefore, we developed a novel strategy that overlaid chemical scaffolds and biological processes, along with empirical hit frequency data, in order to provide a more functional frequent hit triage strategy; the risk of removing biologically relevant frequent hits was reduced compared to the typical empirical hit frequency-based filtering strategy.

In silico fragment-based drug design

Importance of the field: In silico fragment-based drug design (FBDD) is a relatively new approach inspired by the success of the biophysical fragment-based drug discovery field. Here, we review the progress made by this approach in the last decade and showcase how it complements and expands the capabilities of biophysical FBDD and structure-based drug design to generate diverse, efficient drug candidates.Areas covered in this review: Advancements in several areas of research that have enabled the development of in silico FBDD and some applications in drug discovery projects are reviewed.What the reader will gain: The reader is introduced to various computational methods that are used for in silico FBDD, the fragment library composition for this technique, special applications used to identify binding sites on the surface of proteins and how to assess the druggability of these sites. In addition, the reader will gain insight into the proper application of this approach from examples of successful programs.Take home message: In silico FBDD captures a much larger chemical space than high-throughput screening and biophysical FBDD increasing the probability of developing more diverse, patentable and efficient molecules that can become oral drugs. The application of in silico FBDD holds great promise for historically challenging targets such as protein–protein interactions. Future advances in force fields, scoring functions and automated methods for determining synthetic accessibility will all aid in delivering more successes with in silico FBDD.

GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm

In combinatorial chemistry, molecules are assembled according to combinatorial principles by linking suitable reagents or decorating a given scaffold with appropriate substituents from a large chemical space of starting materials. Often the number of possible combinations greatly exceeds the number feasible to handle by an in-depth in silico approach or even more if it should be experimentally synthesized. Therefore, powerful tools to efficiently enumerate large chemical spaces are required. They can be provided by genetic algorithms, which mimic Darwinian evolution. GARLig (genetic algorithm using reagents to compose ligands) has been developed to perform subset selection in large chemical compound spaces subject to target-specific 3D-scoring criteria. GARLig uses different scoring schemes, such as AutoDock4 Score, GOLDScore, and DrugScore(CSD), as fitness functions. Its genetic parameters have been optimized to characterize combinatorial libraries with respect to the binding to various targets of pharmaceutical interest. A large tripeptide library of 20(3) members has been used to profile amino acid frequencies in putative substrates for trypsin, thrombin, factor Xa, and plasmin. A peptidomimetic scaffold assembled from a selection of a 25(3) building block was used to test the performance of the evolutionary algorithm in suggesting potent inhibitors of the enzyme cathepsin D. In a final case study, our program was used to characterize and rank a combinatorial drug-like library comprising 33,750 potential thrombin inhibitors. These case studies demonstrate that GARLig finds experimentally confirmed potent leads by processing a significantly smaller subset of the fully enumerated combinatorial library. Furthermore, the profiles of amino acids computed by the genetic algorithm match the observed amino acid frequencies found by screening peptide libraries in substrate cleavage assays.

In‐silico identification of high potential SSH‐2 specific inhibitors

Cell growth and movement are controlled in part through the activation of a dual specificity phosphatase (DSP) called Slingshot‐2 (SSH‐2). SSH‐2 is known to contribute to the progression of cancer and Alzheimer's disease. For this reason, finding a specific inhibitor for SSH‐2 may have a profound impact in clinical treatments for different diseases. This study eliminates the need to manually test a very large chemical compound space by employing grid computer technologies in virtual molecular docking experiments to identify a list of high potential SSH‐2 inhibitors.Binding affinities were determined in the in‐silico experiments using a previously established grid‐computing workflow that uses the molecular docking simulation software, DOCK 6.0. The results from five DSP screenings, specifically SSH‐2, VHR, VH3, PTEN and KAP, suggest that 3‐[(4,5‐dimethoxy‐3‐oxo‐1H‐isobenzofuran‐1‐yl)amino]‐4‐methyl‐benzoic acid shows the highest affinity for SSH‐2, but lowest affinity for the other DSPs, among the best 100 SSH‐2 binding compounds. These results indicate that this compound has high specificity towards SSH‐2. Although these results must be confirmed through wet bench testing, this study eliminates compounds that are not likely to bind or show high specificity for SSH‐2 and provides a foundation for the analysis of molecular interactions of chemicals with SSH‐2.

Molecular Design of Porphyrin-Based Nonlinear Optical Materials

Nonlinear optical chromophores containing (porphyrinato)Zn(II), proquinoid, and (terpyridyl)metal(II) building blocks were optimized in a library containing approximately 10(6) structures using the linear combination of atomic potentials (LCAP) methodology. We report here the library design and molecular property optimizations. Two basic structural types of large beta(0) chromophores were examined: linear and T-shaped motifs. These T-shaped geometries suggest a promising NLO chromophoric architecture for experimental investigation and further support the value of performing LCAP searches in large chemical spaces.

Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine

Mutagenicity is among the toxicological end points that pose the highest concern. The accelerated pace of drug discovery has heightened the need for efficient prediction methods. Currently, most available tools fall short of the desired degree of accuracy, and can only provide a binary classification. It is of significance to develop a discriminative and informative model for the mutagenicity prediction. Here we developed a mutagenic probability prediction model addressing the problem, based on datasets covering a large chemical space. A novel molecular electrophilicity vector (MEV) is first devised to represent the structure profile of chemical compounds. An extended support vector machine (SVM) method is then used to derive the posterior probabilistic estimation of mutagenicity from the MEVs of the training set. The results show that our model gives a better performance than TOPKAT (http://www.accelrys.com) and other previously published methods. In addition, a confidence level related to the prediction can be provided, which may help people make more flexible decisions on chemical ordering or synthesis. The binary program (ZGTOX_1.1) based on our model and samples of input datasets on Windows PC are available at http://dddc.ac.cn/adme upon request from the authors.

Systematic investigation of protein phase behavior with a microfluidic formulator.

We demonstrated a microfluidic device for rapidly generating complex mixtures of 32 stock reagents in a 5-nl reactor. This "formulation chip" is fully automated and allows thousands of experiments to be performed in a single day with minimal reagent consumption. It was applied to systematically study the phase behavior of the protein xylanase over a large and complex chemical space. For each chemical formulation that demonstrated a pronounced effect on solubility, the protein phase behavior was completely mapped in the chip, generating a set of empirical phase diagrams. This ab initio phase information was used to devise a rational crystallization screen that resulted in 72-fold improvement in successful crystallization hits compared with conventional sparse matrix screens. This formulations tool allows a physics-based approach to protein crystallization that may prove useful in structural genomics efforts.

Bioaccumulation potential of persistent organic chemicals in humans.

A model was used to explore the influence of physicalchemical properties on the potential of organic chemicals to bioaccumulate in humans. ACC-HUMAN, a model of organic chemical bioaccumulation through the agricultural and aquatic food chains to humans, was linked to a level I unit world model of chemical fate in the physical environment and parametrized for conditions in southern Sweden. Hypothetical, fully persistent chemicals with varying physical-chemical properties were distributed in the environment, and their bioaccumulation to humans was calculated. The results were evaluated using the environmental bioaccumulation potential (EBAP), defined as the quotient of the chemical quantity in a human divided by the quantity of chemical in the whole environment. Since the latter is closely related to emissions, EBAP is potentially a more useful tool for comparative risk assessment of chemicals than currently used medium-specific measures such as the fish-water bioaccumulation factor. A high environmental bioaccumulation potential, defined as > 10% of the maximum EBAP, was found for chemicals with 2 < log KOW < 11 and 6 < log KOA < 12. While these chemical partitioning properties clearly influenced bioaccumulation at each trophic level, these effects tended to equalize over the food web. The fact that the transfer from the environment as a whole to humans was quite uniform over a large chemical partitioning space suggests that these partitioning properties are relatively unimportant determinants of human exposure compared to other factors such as the substance's persistence in the environment and in the food web.

Neural networks as data mining tools in drug design

AbstractNeural networks are powerful data mining tools with a wide range of applications in drug design. This paper largely concentrates on self‐organizing neural networks that can be used for investigating datasets both by unsupervised and by supervised learning. The representation of chemical structures is the key to success in establishing useful relationships. Applications are shown for exploring different structure representations, for establishing quantitative structure–activity relationships and for handling compounds having multicategory activities. The applications comprise the separation of compounds according to different biological activities, the location of biologically active compounds in large chemical spaces, the analysis of high‐throughput screening data and the classification of compounds according to mode of toxic action. Copyright © 2003 John Wiley &amp; Sons, Ltd.

Recent Advances in Isocyanide‐Based Multicomponent Chemistry

AbstractFor Abstract see ChemInform Abstract in Full Text.

Solid- and solution-Phase synthesis of highly-Substituted-Pyrrolidine libraries

Starting from a complex bicyclic β-lactam scaffold we have demonstrated the possible production of libraries of a new class of drug-like, highly substituted pyrrolidines. The choice of the type of substitution was made by optimizing various synthetic routes. The selection of each compound is the result of a filtration of a large virtual combinatorial chemical space, using simple criteria. The access to these complex pyrrolidines needed only four to six synthetic steps.