A novel post-synthesis analysis tool is presented which evaluates quality of the organic preparation based on yield, cost, safety, conditions and ease of workup/purification. The proposed approach is based on assigning a range of penalty points to these parameters. This semi-quantitative analysis can easily be modified by other synthetic chemists who may feel that some parameters should be assigned different relative penalty points. It is a powerful tool to compare several preparations of the same product based on safety, economical and ecological features.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

Web services, as an aspect of cloud computing, are becoming an important part of the general IT infrastructure, and scientific computing is no exception to this trend. We propose a simple approach to develop chemical web services, through which servers could expose the essential data manipulation functionality that students and researchers need for chemical calculations. These services return their results as JSON (JavaScript Object Notation) objects, which facilitates their use for web applications. The ChemCalc project demonstrates this approach: we present 3 web services related with mass spectrometry, namely isotopic distribution simulation, peptide fragmentation simulation and molecular formula determination.We also developed a complete web application based on these 3 web services, taking advantage of modern HTML5 and JavaScript libraries (ChemDoodle and jQuery).

For many drugs, finding the balance between efficacy and toxicity requires monitoring their concentrations in the patient's blood. Quantifying drug levels at the bedside or at home would have advantages in terms of therapeutic outcome and convenience, but current techniques require the setting of a diagnostic laboratory. We have developed semisynthetic bioluminescent sensors that permit precise measurements of drug concentrations in patient samples by spotting minimal volumes on paper and recording the signal using a simple point-and-shoot camera. Our sensors have a modular design consisting of a protein-based and a synthetic part and can be engineered to selectively recognize a wide range of drugs, including immunosuppressants, antiepileptics, anticancer agents and antiarrhythmics. This low-cost point-of-care method could make therapies safer, increase the convenience of doctors and patients and make therapeutic drug monitoring available in regions with poor infrastructure.

Aspects of conformational transitions, folding, and misfolding of peptides and proteins have moved into the center of interest in various domains of research at the interface of chemistry, biology, and medicine because of their impact on neurodegenerative diseases.[1] For example, recent research suggests that conformational transitions of soluble amyloid b precursor molecules into aggregated, b-sheet-type forms play a key role in the deposition of cerebral amyloid plaques

This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at .

NMR spectroscopy is certainly the analytical methodology that provides the most information about a molecule but the long-term vision of this information is often lacking. We present here two innovative tools accessible free from the web. NMR assigner allows a chemical structure to be assigned to the corresponding NMR spectrum by simply drawing lines between atoms and automatically characterized signals. NMR resurrector allows NMR spectra to be recreated from published in-line experimental parts enabling the recovery of this lost knowledge.

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