Behind every great research project is great data management.

Research data management (RDM) is the cornerstone of a successful research project, and yet it often remains an underappreciated art that gets overlooked in the hustle and bustle of everyday project management even when required by funding bodies. If researchers are to strive for reproducible science that adheres to the principles of FAIR, then they need to manage the data associated with their research projects effectively. It is imperative to plan your RDM strategies early on, and setup your project organisation before embarking on the work. There are several different factors to consider: data management plans, data organisation and storage, publishing and sharing your data, ensuring reproducibility and adhering to data standards. Additionally it is important to reflect upon the ethical implications that might need to be planned for, and adverse issues that may need a mitigation strategy. This short article discusses these different areas, noting some best practices and detailing how to incorporate these strategies into your work. Finally, the article ends with a set of top ten tips for effective research data management.

Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier.

We present a sequence-to-sequence machine learning model for predicting the IUPAC name of a chemical from its standard International Chemical Identifier (InChI). The model uses two stacks of transformers in an encoder-decoder architecture, a setup similar to the neural networks used in state-of-the-art machine translation. Unlike neural machine translation, which usually tokenizes input and output into words or sub-words, our model processes the InChI and predicts the IUPAC name character by character. The model was trained on a dataset of 10 million InChI/IUPAC name pairs freely downloaded from the National Library of Medicine's online PubChem service. Training took seven days on a Tesla K80 GPU, and the model achieved a test set accuracy of 91%. The model performed particularly well on organics, with the exception of macrocycles, and was comparable to commercial IUPAC name generation software. The predictions were less accurate for inorganic and organometallic compounds. This can be explained by inherent limitations of standard InChI for representing inorganics, as well as low coverage in the training data.

QSAR analysis of substituent effects on tambjamine anion transporters.

The transmembrane anion transport activity of 43 synthetic molecules based on the structure of marine alkaloid tambjamine were assessed in model phospholipid (POPC) liposomes. The anionophoric activity of these molecules showed a parabolic dependence with lipophilicity, with an optimum range for transport efficiency. Using a quantitative structure-transport activity (QSAR) approach it was possible to rationalize these results and to quantify the contribution of lipophilicity to the transport activity of these derivatives. While the optimal value of log P and the curvature of the parabolic dependence is a property of the membrane (and so similar for the different series of substituents) we found that for relatively simple substituents in certain locations on the tambjamine core, hydrophobic interactions clearly dominate, but for others, more specific interactions are present that change the position of the membrane hydrophobicity parabolic envelope.