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.

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.

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.