![Overview. (a) Composition of a bacterial cell by weight, with a breakdown of the chemical constituents [21]. About 40% of these compounds are not properly covered in existing datasets. (b) Multi-step workflow used to construct QCell, beginning with the selection of building blocks, followed by conformational sampling and fragment selection, pre-optimization with DFTB+MBD, and finally hybrid PBE0+MBD(-NL) calculations. (c) Coverage of molecular species at the PBE0+MBD(-NL) level of theory, including entries from existing databases and newly generated QCell data for nucleic acid fragments, lipids, sugars, solvated ions, and dimers. -- AI for Science](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/04/Mapping-The-Molecules-Of-Life.png?resize=1200%2C719&ssl=1)
Machine learning force fields (MLFFs) are rapidly transforming molecular simulations by combining the accuracy of quantum mechanics with the speed of classical approaches. However, training reliable MLFFs for biological systems […]
The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets.
Editor’s note: If we aspire to mount expeditions to new worlds and then embrace the task of characterizing and quantifying whatever life forms we find, the ability to map and […]
The exoplanet archive is an incredible resource of information on the properties of discovered extrasolar planets, but statistical analysis has been limited by the number of missing values. One of […]
Spaceflight studies present novel insights into biological processes through exposure to stressors outside the evolutionary path of terrestrial organisms. Despite limited access to space environments, numerous transcriptomic datasets from spaceflight […]
The interdisciplinary nature of astrobiology presents challenges to data management, integration, and analysis because of its great diversity.
