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Open PHACTS is a public-private partnership between academia, publishers, small and medium sized enterprises and pharmaceutical companies. The goal of the project is to deliver and sustain an 'open pharmacological space' using and enhancing state-of-the-art semantic web standards and technologies. It is focused on practical and robust applications to solve specific questions in drug discovery research. OPS is intended to facilitate improvements in drug discovery in academia and industry and to support open innovation and in-house non-public drug discovery research. This paper lays out the challenges and how the Open PHACTS project is hoping to address these challenges technically and socially., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The proliferation of mobile devices such as smartphones and tablet computers has recently been extended to include a growing ecosystem of increasingly sophisticated chemistry software packages, commonly known as apps. The capabilities that these apps can offer to the practicing chemist are approaching those of conventional desktop-based software, but apps tend to be focused on a relatively small range of tasks. To overcome this, chemistry apps must be able to seamlessly transfer data to other apps, and through the network to other devices, as well as to other platforms, such as desktops and servers, using documented file formats and protocols whenever possible. This article describes the development and state of the art with regard to chemistry-aware apps that make use of facile data interchange, and some of the scenarios in which these apps can be inserted into a chemical information workflow to increase productivity. A selection of contemporary apps is used to demonstrate their relevance to pharmaceutical research. Mobile apps represent a novel approach for delivery of cheminformatics tools to chemists and other scientists, and indications suggest that mobile devices represent a disruptive technology for drug discovery, as they have been to many other industries.

The Open Drug Discovery Teams (ODDT) project provides a mobile app primarily intended as a research topic aggregator of predominantly open science data collected from various sources on the internet. It exists to facilitate interdisciplinary teamwork and to relieve the user from data overload, delivering access to information that is highly relevant and focused on their topic areas of interest. Research topics include areas of chemistry and adjacent molecule-oriented biomedical sciences, with an emphasis on those which are most amenable to open research at present. These include rare and neglected diseases, and precompetitive and public-good initiatives such as green chemistry. The ODDT project uses a free mobile app as user entry point. The app has a magazine-like interface, and server-side infrastructure for hosting chemistry-related data as well as value added services. The project is open to participation from anyone and provides the ability for users to make annotations and assertions, thereby contributing to the collective value of the data to the engaged community. Much of the content is derived from public sources, but the platform is also amenable to commercial data input. The technology could also be readily used in-house by organizations as a research aggregator that could integrate internal and external science and discussion. The infrastructure for the app is currently based upon the Twitter API as a useful proof of concept for a real time source of publicly generated content. This could be extended further by accessing other APIs providing news and data feeds of relevance to a particular area of interest. As the project evolves, social networking features will be developed for organizing participants into teams, with various forms of communication and content management possible.

Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P-glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP-search open source databases. Furthermore, the ability of data, determined in different assays, to be combined with each other is explored. As a result of this study, it is suggested that for inhibitors of human P-glycoprotein it is possible to combine data coming from the same assay type, if the cell lines used are also identical and the fluorescent or radiolabeled substrate have overlapping binding sites. In addition, it demonstrates that there is a need for larger chemical diverse datasets that have been measured in a panel of different assays. This would certainly alleviate the search for other inter-correlations between bioactivity data yielded by different assay setups.

In recent years there has been a dramatic increase in the number of freely accessible online databases serving the chemistry community. The internet provides chemistry data that can be used for data-mining, for computer models, and integration into systems to aid drug discovery. There is however a responsibility to ensure that the data are high quality to ensure that time is not wasted in erroneous searches, that models are underpinned by accurate data and that improved discoverability of online resources is not marred by incorrect data. In this article we provide an overview of some of the experiences of the authors using online chemical compound databases, critique the approaches taken to assemble data and we suggest approaches to deliver definitive reference data sources., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Background: One of the largest challenges in chemistry today remains that of efficiently mining through vast amounts of data in order to elucidate the chemical structure for an unknown compound. The elucidated candidate compound must be fully consistent with the data and any other competing candidates efficiently eliminated without doubt by using additional data if necessary. It has become increasingly necessary to incorporate an in silico structure generation and verification tool to facilitate this elucidation process. An effective structure elucidation software technology aims to mimic the skills of a human in interpreting the complex nature of spectral data while producing a solution within a reasonable amount of time. This type of software is known as computer-assisted structure elucidation or CASE software. A systematic trial of the ACD/Structure Elucidator CASE software was conducted over an extended period of time by analysing a set of single and double-blind trials submitted by a global audience of scientists. The purpose of the blind trials was to reduce subjective bias. Double-blind trials comprised of data where the candidate compound was unknown to both the submitting scientist and the analyst. The level of expertise of the submitting scientist ranged from novice to expert structure elucidation specialists with experience in pharmaceutical, industrial, government and academic environments., Results: Beginning in 2003, and for the following nine years, the algorithms and software technology contained within ACD/Structure Elucidator have been tested against 112 data sets; many of these were unique challenges. Of these challenges 9% were double-blind trials. The results of eighteen of the single-blind trials were investigated in detail and included problems of a diverse nature with many of the specific challenges associated with algorithmic structure elucidation such as deficiency in protons, structure symmetry, a large number of heteroatoms and poor quality spectral data., Conclusion: When applied to a complex set of blind trials, ACD/Structure Elucidator was shown to be a very useful tool in advancing the computer's contribution to elucidating a candidate structure from a set of spectral data (NMR and MS) for an unknown. The synergistic interaction between humans and computers can be highly beneficial in terms of less biased approaches to elucidation as well as dramatic improvements in speed and throughput. In those cases where multiple candidate structures exist, ACD/Structure Elucidator is equipped to validate the correct structure and eliminate inconsistent candidates. Full elucidation can generally be performed in less than two hours; this includes the average spectral data processing time and data input.

In many cases, an unknown to an investigator is actually known in the chemical literature, a reference database, or an internet resource. We refer to these types of compounds as "known unknowns." ChemSpider is a very valuable internet database of known compounds useful in the identification of these types of compounds in commercial, environmental, forensic, and natural product samples. The database contains over 26 million entries from hundreds of data sources and is provided as a free resource to the community. Accurate mass mass spectrometry data is used to query the database by either elemental composition or a monoisotopic mass. Searching by elemental composition is the preferred approach. However, it is often difficult to determine a unique elemental composition for compounds with molecular weights greater than 600 Da. In these cases, searching by the monoisotopic mass is advantageous. In either case, the search results are refined by sorting the number of references associated with each compound in descending order. This raises the most useful candidates to the top of the list for further evaluation. These approaches were shown to be successful in identifying "known unknowns" noted in our laboratory and for compounds of interest to others., (© American Society for Mass Spectrometry, 2011)

When we look at the rapid growth of scientific databases on the Internet in the past decade, we tend to take the accessibility and provenance of the data for granted. As we see a future of increased database integration, the licensing of the data may be a hurdle that hampers progress and usability. We have formulated four rules for licensing data for open drug discovery, which we propose as a starting point for consideration by databases and for their ultimate adoption. This work could also be extended to the computational models derived from such data. We suggest that scientists in the future will need to consider data licensing before they embark upon re-using such content in databases they construct themselves.

Toxicity data is expensive to generate, is increasingly seen as precompetitive, and is frequently used for the generation of computational models in a discipline known as computational toxicology. Repositories of chemical property data are valuable for supporting computational toxicologists by providing access to data regarding potential toxicity issues with compounds as well as for the purpose of building structure-toxicity relationships and associated prediction models. These relationships use mathematical, statistical, and modeling computational approaches and can be used to understand the mechanisms by which chemicals cause harm and, ultimately, enable prediction of adverse effects of these chemicals to human health and/or the environment. Such approaches are of value as they offer an opportunity to prioritize chemicals for testing. An increasing amount of data used by computational toxicologists is being published into the public domain and, in parallel, there is a greater availability of Open Source software for the generation of computational models. This chapter provides an overview of the types of data and software available and how these may be used to produce predictive toxicology models for the community.

Mobile hardware and software technology continues to evolve very rapidly and presents drug discovery scientists with new platforms for accessing data and performing data analysis. Smartphones and tablet computers can now be used to perform many of the operations previously addressed by laptops or desktop computers. Although the smaller screen sizes and requirements for touch-screen manipulation can present user-interface design challenges, especially with chemistry-related applications, these limitations are driving innovative solutions. In this early review of the topic, we collectively present our diverse experiences as software developer, chemistry database expert and naïve user, in terms of what mobile platforms could provide to the drug discovery chemist in the way of applications in the future as this disruptive technology takes off., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

From research published in the last six years we have identified 34 studies that have screened libraries of FDA-approved drugs against various whole cell or target assays. These studies have each identified one or more compounds with a suggested new bioactivity that had not been described previously. We now show that 13 of these drugs were active against more than one additional disease, thereby suggesting a degree of promiscuity. We also show that following compilation of all the studies, 109 molecules were identified by screening in vitro. These molecules appear to be statistically more hydrophobic with a higher molecular weight and AlogP than orphan-designated products with at least one marketing approval for a common disease indication or one marketing approval for a rare disease from the FDA's rare disease research database. Capturing these in vitro data on old drugs for new uses will be important for potential reuse and analysis by others to repurpose or reposition these or other existing drugs. We have created databases which can be searched by the public and envisage that these can be updated as more studies are published.

One approach to speed up drug discovery is to examine new uses for existing approved drugs, so-called 'drug repositioning' or 'drug repurposing', which has become increasingly popular in recent years. Analysis of the literature reveals many examples of US Food and Drug Administration-approved drugs that are active against multiple targets (also termed promiscuity) that can also be used to therapeutic advantage for repositioning for other neglected and rare diseases. Using proof-of-principle examples, we suggest here that with current in silico technologies and databases of the structures and biological activities of chemical compounds (drugs) and related data, as well as close integration with in vitro screening data, improved opportunities for drug repurposing will emerge for neglected or rare/orphan diseases., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to results for internal validation. The Bayesian model with extended connectivity functional class fingerprints of maximum diameter 6 (ECFC_6) and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g., ketones, diols, and α-methyl styrene type structures. Using Smiles Arbitrary Target Specification (SMARTS) filters published by several pharmaceutical companies, we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, such as the Abbott alerts, which captures thiol traps and other compounds, may be of use in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies. These computational models may represent cost-effective selection criteria before in vitro or in vivo experimental studies.

The recent announcement that GlaxoSmithKline have released a huge tranche of whole-cell malaria screening data to the public domain, accompanied by a corresponding publication, raises some issues for consideration before this exemplar instance becomes a trend. We have examined the data from a high level, by studying the molecular properties, and consider the various alerts presently in use by major pharma companies. We not only acknowledge the potential value of such data but also raise the issue of the actual value of such datasets released into the public domain. We also suggest approaches that could enhance the value of such datasets to the community and theoretically offer an immediate benefit to the search for leads for other neglected diseases., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Background: Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships., Results: We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation., Conclusions: We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist.

Web-based technologies coupled with a drive for improved communication between scientists have resulted in the proliferation of scientific opinion, data and knowledge at an ever-increasing rate. The increasing array of chemistry-related computer-based resources now available provides chemists with a direct path to the discovery of information, once previously accessed via library services and limited to commercial and costly resources. We propose that preclinical absorption, distribution, metabolism, excretion and toxicity data as well as pharmacokinetic properties from studies published in the literature (which use animal or human tissues in vitro or from in vivo studies) are precompetitive in nature and should be freely available on the web. This could be made possible by curating the literature and patents, data donations from pharmaceutical companies and by expanding the currently freely available ChemSpider database of over 21 million molecules with physicochemical properties. This will require linkage to PubMed, PubChem and Wikipedia as well as other frequently used public databases that are currently used, mining the full text publications to extract the pertinent experimental data. These data will need to be extracted using automated and manual methods, cleaned and then published to the ChemSpider or other database such that it will be freely available to the biomedical research and clinical communities. The value of the data being accessible will improve development of drug molecules with good ADME/Tox properties, facilitate computational model building for these properties and enable researchers to not repeat the failures of past drug discovery studies.

We report on the implementation of the Spectral Game, a web-based game where players try to match molecules to various forms of interactive spectra including 1D/2D NMR, Mass Spectrometry and Infrared spectra. Each correct selection earns the player one point and play continues until the player supplies an incorrect answer. The game is usually played using a web browser interface, although a version has been developed in the virtual 3D environment of Second Life. Spectra uploaded as Open Data to ChemSpider in JCAMP-DX format are used for the problem sets together with structures extracted from the website. The spectra are displayed using JSpecView, an Open Source spectrum viewing applet which affords zooming and integration. The application of the game to the teaching of proton NMR spectroscopy in an undergraduate organic chemistry class and a 2D Spectrum Viewer are also presented.

The reliable determination of stereocenters contained within chemical structures usually requires utilization of NMR data, chemical derivatization, molecular modeling, quantum-mechanical (QM) calculations and, if available, X-ray analysis. In this article, we show that the number of stereoisomers which need to be thoroughly verified, can be significantly reduced by the application of NMR chemical shift calculation to the full stereoisomer set of possibilities using a fragmental approach based on HOSE codes. The applicability of this suggested method is illustrated using experimental data published for a series of complex chemical structures., (Copyright (c) 2009 John Wiley & Sons, Ltd.)

Background: This article coincides with the 40 year anniversary of the first published works devoted to the creation of algorithms for computer-aided structure elucidation (CASE). The general principles on which CASE methods are based will be reviewed and the present state of the art in this field will be described using, as an example, the expert system Structure Elucidator., Results: The developers of CASE systems have been forced to overcome many obstacles hindering the development of a software application capable of drastically reducing the time and effort required to determine the structures of newly isolated organic compounds. Large complex molecules of up to 100 or more skeletal atoms with topological peculiarity can be quickly identified using the expert system Structure Elucidator based on spectral data. Logical analysis of 2D NMR data frequently allows for the detection of the presence of COSY and HMBC correlations of "nonstandard" length. Fuzzy structure generation provides a possibility to obtain the correct solution even in those cases when an unknown number of nonstandard correlations of unknown length are present in the spectra. The relative stereochemistry of big rigid molecules containing many stereocenters can be determined using the StrucEluc system and NOESY/ROESY 2D NMR data for this purpose., Conclusion: The StrucEluc system continues to be developed in order to expand the general applicability, provide improved workflows, usability of the system and increased reliability of the results. It is expected that expert systems similar to that described in this paper will receive increasing acceptance in the next decade and will ultimately be integrated directly to analytical instruments for the purpose of organic analysis. Work in this direction is in progress. In spite of the fact that many difficulties have already been overcome to deliver on the spectroscopist's dream of "fully automated structure elucidation" there is still work to do. Nevertheless, as the efficiency of expert systems is enhanced the solution of increasingly complex structural problems will be achievable.

Long-range homonuclear coupling pathways can be observed in COSY or GCOSY spectra by the acquisition of spectra with larger numbers of increments of the evolution period, t(1), than would normally be used. Alternatively, covariance processing of COSY-type spectra acquired with modest numbers of t(1) increments, allows the observation of multistage correlations. In this work results obtained from covariance-processed GCOSY spectra are fully analyzed and compared to normally processed COSY and 80 ms TOCSY spectra. Multistage or 'RCOSY-type' correlations are observed when remote protons both exhibit correlations to the same coupling partner e.g. A --> B and B --> C gives rise to an A --> C correlation. In the strict sense, RCOSY-type responses are artifacts albeit providing useful information. Nonbeneficial artifact correlations are observed when protons couple to other protons that overlap or partially overlap. The origin of artifact responses is also analyzed., (2008 John Wiley & Sons, Ltd.)

The Internet has spawned access to unprecedented levels of information. For chemists the increasing number of resources they can use to access chemistry-related information provides them a valuable path to discovery of information, one which was previously limited to commercial and therefore constrained resources. The diversity of information continues to expand at a dramatic rate and, coupled with an increasing awareness for quality, curation and improved tools for focused searches, chemists are now able to find valuable information within a few seconds using a few keystrokes. This shift to publicly available resources offers great promise to the benefits of science and society yet brings with it increasing concern from commercial entities. This article will discuss the benefits and disruptions associated with an increase in publicly available scientific resources.

Web-based technologies, coupled with a drive for improved communication between scientists, have resulted in the proliferation of scientific opinion, data and knowledge at an ever-increasing rate. The availability of tools to host wikis and blogs has provided the necessary building blocks for scientists with only a rudimentary understanding of computer software science to communicate to the masses. This newfound freedom has the ability to speed up research and sharing of results, develop extensive collaborations, conduct science in public, and in near-real time. The technologies supporting chemistry, while immature, are fast developing to support chemical structures and reactions, analytical data support and integration to related data sources via supporting software technologies. Communication in chemistry is already witnessing a new revolution.

Several groups of authors have reported studies in the areas of indirect and unsymmetrical indirect covariance NMR processing methods. Efforts have recently focused on the use of unsymmetrical indirect covariance processing methods to combine various discrete two-dimensional NMR spectra to afford the equivalent of the much less sensitive hyphenated 2D NMR experiments, for example indirect covariance (icv)-heteronuclear single quantum coherence (HSQC)-COSY and icv-HSQC-nuclear Overhauser effect spectroscopy (NOESY). Alternatively, unsymmetrical indirect covariance processing methods can be used to combine multiple heteronuclear 2D spectra to afford icv-13C-15N HSQC-HMBC correlation spectra. We now report the use of responses contained in indirect covariance processed HSQC spectra as a means for the identification of artifacts in both indirect covariance and unsymmetrical indirect covariance processed 2D NMR spectra., (Copyright (c) 2007 John Wiley & Sons, Ltd.)

The efficacy of neural network (NN) and partial least-squares (PLS) methods is compared for the prediction of NMR chemical shifts for both 1H and 13C nuclei using very large databases containing millions of chemical shifts. The chemical structure description scheme used in this work is based on individual atoms rather than functional groups. The performances of each of the methods were optimized in a systematic manner described in this work. Both of the methods, least-squares and neural network analyses, produce results of a very similar quality, but the least-squares algorithm is approximately 2--3 times faster.

Unsymmetrical indirect covariance processing methods allow the derivation of hyphenated 2D NMR data from the component 2D spectra, potentially circumventing the acquisition of the much lower sensitivity hyphenated 2D NMR experimental data. Calculation of HSQC-COSY and HSQC-NOESY spectra from GHSQC, COSY, and NOESY spectra, respectively, has been reported. The use of unsymmetrical indirect covariance processing has also been applied to the combination of (1)H- (13)C GHSQC and (1)H- (15)N long-range correlation data (GHMBC, IMPEACH, or CIGAR-HMBC). The application of unsymmetrical indirect covariance processing to spectra of vinblastine is now reported, specifically the algorithmic extraction of (13)C- (15)N correlations via the unsymmetrical indirect covariance processing of the combination of (1)H- (13)C GHSQC and long-range (1)H- (15)N GHMBC to produce the equivalent of a (13)C- (15)N HSQC-HMBC correlation spectrum. The elimination of artifact responses with aromatic solvent-induced shifts (ASIS) is shown in addition to a method of forecasting potential artifact responses through the indirect covariance processing of the GHSQC spectrum used in the unsymmetrical indirect covariance processing.

Utilization of long-range (1)H--(15)N heteronuclear chemical shift correlation has continually grown in importance since the first applications were reported in 1995. More recently, indirect covariance NMR methods have been introduced followed by the development of unsymmetrical indirect covariance processing methods. The latter technique has been shown to allow the calculation of hyphenated 2D NMR data matrices from more readily acquired nonhyphenated 2D NMR spectra. We recently reported the use of unsymmetrical indirect covariance processing to combine (1)H--(13)C GHSQC and (1)H--(15)N GHMBC long-range spectra to yield a (13)C--(15)N HSQC-HMBC chemical shift correlation spectrum that could not be acquired in a reasonable period of time without resorting to (15)N-labeled molecules. We now report the unsymmetrical indirect covariance processing of (1)H--(13)C GHMBC and (1)H--(15)N IMPEACH spectra to afford a (13)C--(15)N HMBC-IMPEACH spectrum that has the potential to span as many as six to eight bonds. Correlations for carbon resonances long-range coupled to a protonated carbon in the (1)H--(13)C HMBC spectrum are transferred via the long-range (1)H--(15)N coupling pathway in the (1)H--(15)N IMPEACH spectrum to afford a much broader range of correlation possibilities in the (13)C--(15)N HMBC-IMPEACH correlation spectrum. The indole alkaloid vincamine is used as a model compound to illustrate the application of the method., ((c) 2007 John Wiley & Sons, Ltd.)

A method for structure validation based on the simultaneous analysis of a 1D (1)H NMR and 2D (1)H - (13)C single-bond correlation spectrum such as HSQC or HMQC is presented here. When compared with the validation of a structure by a 1D (1)H NMR spectrum alone, the advantage of including a 2D HSQC spectrum in structure validation is that it adds not only the information of (13)C shifts, but also which proton shifts they are directly coupled to, and an indication of which methylene protons are diastereotopic. The lack of corresponding peaks in the 2D spectrum that appear in the 1D (1)H spectrum, also gives a clear picture of which protons are attached to heteroatoms. For all these benefits, combined NMR verification was expected and found by all metrics to be superior to validation by 1D (1)H NMR alone. Using multiple real-life data sets of chemical structures and the corresponding 1D and 2D data, it was possible to unambiguously identify at least 90% of the correct structures. As part of this test, challenging incorrect structures, mostly regioisomers, were also matched with each spectrum set. For these incorrect structures, the false positive rate was observed as low as 6%., ((c) 2007 John Wiley & Sons, Ltd.)

GHSQC-TOCSY experiments allow sorting of proton-proton connectivity information as a function of (13)C chemical shift. GHSQC-TOCSY is a relatively insensitive 2D NMR experiment. Given two coherence transfer experiments, A --> B and A --> C, it is possible to indirectly determine B <--> C. Unsymmetrical indirect covariance processing of a (1)H- (13)C GHSQC and a GCOSY spectrum afforded a GHSQC-COSY spectrum, with an information content analogous to a GHSQC-TOCSY experiment. However, GHSQC-TOCSY is of significantly lower sensitivity and the data require considerably more time to acquire than either of the component experiments. Investigators needing access to GHSQC-TOCSY type data can, in principle, access it from more readily acquired 2D NMR data. Strychnine ( 1) was used as a model compound to illustrate this capability.

There has been considerable interest over the past decade in the utilization of direct and long-range 1H- 15N heteronuclear shift correlation methods at natural abundance to facilitate the elucidation of small molecule structures. Recently, there has also been a high level of interest in the exploration of indirect covariance NMR methods. Our initial explorations in this area led to the development of unsymmetrical indirect covariance methods, which allow the calculation of hyphenated 2D NMR spectra such as 2D GHSQC-COSY and GHSQC-NOESY from the discrete component 2D NMR experiments. We now wish to report the utilization of unsymmetrical indirect covariance NMR methods for the combination of 1H- 13C GHSQC and 1H- 15N long-range (GHMBC, IMPEACH-MBC, CIGAR-HMBC, etc.) heteronuclear chemical shift correlation spectra to determine 15N- 13C correlation pathways., (Copyright 2007 John Wiley & Sons, Ltd.)

We have recently demonstrated that unsymmetrical indirect covariance NMR methods can be used to mathematically calculate the equivalent of low sensitivity, hyphenated NMR experiments by combining data from a pair of higher sensitivity experiments. The present report demonstrates the application of this method to the combination of HSQC and NOESY spectra to provide results comparable to HSQC-NOESY data, albeit with greater sensitivity and with considerably less spectrometer time., (Copyright (c) 2007 John Wiley & Sons, Ltd.)

Contemporary Computer-Aided Structure Elucidation (CASE) systems are heavily based on the utilization of 2D NMR spectra. The utilization of HMBC/GHMBC and COSY/GCOSY correlations generally assumes that these correlations result from (2-3)JCH and (2-3)JHH spin-spin couplings, respectively, and consequently these values are used as the default setting in these systems. Our previous studies1,2 have shown that about half of the problems studied actually contain some correlations of 4-6 bonds, so-called "nonstandard" correlations. In such cases the initial 2D NMR data are contradictory, and the correct solution is therefore not directly attainable. Unfortunately nonstandard correlations and the number of intervening bonds usually cannot be identified experimentally. In this work we suggest a new approach that we term Fuzzy Structure Generation. This allows the solution of structural problems whose 2D NMR data contain an unknown number of nonstandard correlations having different and unknown lengths. Suggested methods for the application of Fuzzy Structure Generation are described, and their application is illustrated by a series of real-world examples. We conclude that Fuzzy Structure Generation is efficient, and there is no real alternative at present in terms of a universal practical method for the structure elucidation of organic molecules from 2D NMR data.

Expert systems for spectroscopic molecular structure elucidation have been developed since the mid-1960s. Algorithms associated with the structure generation process within these systems are deterministic; that is, they are based on graph theory and combinatorial analysis. A series of expert systems utilizing 2D NMR spectra have been described in the literature and are capable of determining the molecular structures of large organic molecules including complex natural products. Recently, an opinion was expressed in the literature that these systems would fail when elucidating structures containing more than 30 heavy atoms. A suggestion was put forward that stochastic algorithms for structure generation would be necessary to overcome this shortcoming. In this article, we describe a comprehensive investigation of the capabilities of the deterministic expert system Structure Elucidator. The results of performing the structure elucidation of 250 complex natural products with this program were studied and generalized. The conclusion is that 2D NMR deterministic expert systems are certainly capable of elucidating large structures (up to about 100 heavy atoms) and can deal with the complexities associated with both poor and contradictory spectral data.

The preferential sorption of model compounds to calcium-exchanged montmorillonite surfaces was investigated using 1H high-resolution magic-angle spinning (HR-MAS) and liquid-state NMR. Synthetic mixtures, representing the major structural categories abundant in natural organic matter (NOM), and two soil extracts were sorbed to montmorillonite. The NMR spectra indicate that, of the organic components observable by 1H HR-MAS NMR, aliphatic components preferentially sorb to the clay surface, while carbohydrates and amino acids mainly remain in the supernatant. These results may help explain the highly aliphatic nature of organic matter associated with clay fractions in natural soils and sediments. Investigations using the synthetic mixtures demonstrate a specific interaction between the clay surface and the polar region in 1-palmitoyl-3-stearoyl-rac-glycerol. Similar observations were obtained with natural soil extracts. The results presented have important implications for understanding the role of organoclay complexes in natural processes, and provides preliminary evidence that HR-MAS NMR is a powerful analytical technique for the investigation of organoclay complex structure and conformation.

This review summarizes progress made in the development of fully computational approaches to the prediction of blood-brain barrier (BBB) permeability of small molecules, with a focus on rapid computational methods suitable for the analysis of large compound sets and virtual screening. A comparative analysis using the recently developed Advanced Chemistry Development (ACD/Labs) Inc BBB permeability algorithm for the calculation of logBB values for known Alzheimer's disease medicines, selected central nervous system drugs and new secretase inhibitors for Alzheimer's disease, is presented. The trends in logBB values and the associated physiochemical properties of these agents as they relate to the potential for BBB permeability are also discussed.

A unique opportunity exists when an experimental NMR spectrum is obtained for which a specific chemical structure is anticipated. A process of Verification--the confirmation of a postulated structure--is now possible, as opposed to Elucidation-the de novo determination of a structure. A method for automated structure verification is suggested, which compares the chemical shifts, intensities and multiplicities of signals in an experimental 1H NMR spectrum with those from a predicted spectrum for the proposed structure. A match factor (MF) is produced and used to classify the spectrum-structure match into one of three categories, correct, ambiguous, or incorrect. The verification result is also augmented by the spectrum assignment obtained as part of the verification process. This method was tested on a set of synthetic spectra and several sets of experimental spectra, all of which were automatically prepared from raw data. Taking into account even the most problematic structures, with many labile protons present and poor prediction accuracy, 50% of all spectra can still be automatically verified without any false positives or negatives. In a blind test on a typical set of data, it is shown that fewer than 31% of the structures would need manual evaluation. This means that a system is possible whereby 69% of the spectra are prepared and evaluated automatically, and never need to be seen or evaluated by a human., (Copyright 2006 John Wiley & Sons, Ltd.)

Recently it has been shown that urban surfaces are covered with a thin film which mediates the fate, distribution and accumulation of semi-volatile organic compounds in the environment. In this study we apply a combination of solution, semi-solids, and solid state nuclear magnetic resonance (NMR) methods to provide a general overview of the organic constituents. In surface film collected from 30 m2 of outside windows over an area of 12 km2 in downtown Toronto, we roughly estimate that the organic carbon is approximately 35% carbohydrate, approximately 35% aliphatics, approximately 20% aromatics, and approximately 10% carbonyl groups. Various aliphatic groups can be identified including a number of acids, alcohols, alkanes, and alkenes. Also, numerous intact aliphatic esters are apparent that have not been observed before, as well as carbohydrates. The aromatic species include a small portion that appears to be derived from a polymer of styrene, in addition a larger fraction is consistent with polyhydroxylated PAH derived material, although this assignment is tentative and based solely on 1-D NMR data only. In addition, signals from polybutadiene are present and while accurate quantification is not possible, it appears that this polymer may be up to a few percents by weight of the total organic material.

It was recently demonstrated that an IDR- (Inverted Direct Response) HSQC-TOCSY data set could be decomposed into a negatively phased direct response spectrum and a positively phased relayed response spectrum that could then be subjected to unsymmetrical indirect covariance processing for the removal of artifacts due to response overlap in the proton NMR spectrum of the molecule. Using experimentally discrete HSQC and HMBC data sets, it is shown that unsymmetrical indirect covariance processing of the pair of NMR spectra affords a presentation containing long-range carbon-carbon connectivity information. The method is demonstrated using strychnine as a model compound. The resulting data are largely free of artifacts although artifacts can arise due to proton response overlap, as previously reported., (Copyright 2005 John Wiley & Sons, Ltd.)

Indirect covariance NMR offers an alternative method of extracting spin-spin connectivity information via the conversion of an indirect-detection heteronuclear shift-correlation data matrix to a homonuclear data matrix. Using an IDR (inverted direct response)-HSQC-TOCSY spectrum as a starting point for the indirect covariance processing, a spectrum that can be described as a carbon-carbon COSY experiment is obtained. These data are analogous to the autocorrelated 13C-13C double quantum INADEQUATE experiment except that the indirect covariance NMR spectrum establishes carbon-carbon connectivities only between contiguous protonated carbons. Cyclopentafuranone and the complex polynuclear heteroaromatic naphtho[2',1':5,6]-naphtho[2',1':4,5]thieno[2,3-c]quinoline are used as model compounds. The former is a straightforward example because of its well-resolved proton spectrum, while the latter, which has considerable resonance overlap in its congested proton spectrum, gives rise to two types of artifact responses that must be considered when using the indirect covariance NMR method., (Copyright (c) 2005 John Wiley & Sons, Ltd.)

The elucidation of chemical structures from 2D NMR data commonly utilizes a combination of COSY, HMQC/HSQC, and HMBC data. Generally COSY connectivities are assumed to mostly describe the separation of protons that are separated by 1 skeletal bond (3JHH), while HMBC connectivities represent protons separated from carbon atoms by 1 to 2 skeletal bonds (2JCH and 3JCH). Obviously COSY and HMBC connectivities of lengths greater than those described have been detected. Though experimental techniques have recently been described to aid in the identification of the nature of the couplings the detection of whether a coupling is 2-bond or greater still remains a challenge in most laboratories. In the StrucEluc software system the common lengths of the connectivities, 1-bond for COSY and 1- or 2-bond for HMBC, derived from 2D NMR data are set as the default. Therefore, in the presence of any extended connectivities contradictions can appear in the 2D NMR data. In this article, algorithmic methods for the detection and removal of contradictions in 2D NMR data that have been developed in support of StrucEluc are described. The methods are based on the analysis of molecular connectivity diagrams, MCDs. These methods have been implemented in the StrucEluc system and tested by solving 50 structural problems with 2D NMR spectral data containing contradictions. The presence of contradictions was detected by the algorithm in 90% of the cases, and the contradictions were automatically removed in approximately 50% of the problems. A method of "fuzzy" structure generation in the presence of contradictions has been suggested and successfully tested in this work. This work will demonstrate examples of the application of developed methods to a number of structural problems., (Copyright 2004 American Chemical Society)

StrucEluc is an expert system that allows the computer-assisted elucidation of chemical structures based on the inputs of a series of spectral data including 1D and 2D NMR and mass spectra. The system has been enabled to allow a chemist to utilize fragments stored in a fragment database as well as user-defined fragments submitted by the chemist in the structure elucidation process. The association of fragments in this way has been shown to dramatically speed up the process of structure generation from 2D NMR data and has helped to minimize or eliminate the need for user intervention thereby further enabling the vision of automated elucidation. The use of fragments has frequently transformed very difficult 2D NMR elucidation challenges into easily solvable tasks. A strategy to utilize molecular fragments has been developed and optimized based on specific challenging examples. This strategy will be described here using real world examples. Experience gained by solving more than 150 structure elucidation problems from a variety of literature sources is also reviewed in this work.

Described herein are applications of the latest version of the StrucEluc expert software system, enhanced to use 2D NMR data, to the structure elucidation of 60 recently isolated natural products. In this study, selected molecules containing between 15 and 65 skeletal atoms and having molecular masses ranging from 200 to 900 amu have been investigated. The correct structure was determined unambiguously for 58 of these molecules. The structures for 75% of the data sets were determined in less than one minute, while 90% of the analyses required no more than 30 minutes. The strategy of structure elucidation by this expert system is described, and several examples are discussed. These illustrate that StrucEluc is a powerful and versatile analytical tool for the structure elucidation of natural products.