Drylabs

Dry labs are laboratories in which investigations rely primarily on computer-based simulations, calculations, and data analysis rather than hands-on experiments with wet reagents or biological materials. They may operate as standalone facilities or as dedicated spaces within larger research institutions, integrated with traditional laboratories.

Typical activities in dry labs include computational modeling and simulations (such as molecular dynamics or quantum chemistry), data mining and statistics, bioinformatics, algorithm development, machine learning and artificial intelligence, software tooling for experimental design, data visualization, and data curation. Equipment centers on high-performance computing clusters, servers, and cloud computing platforms, rather than pipettes, glassware, or incubators.

Dry labs frequently complement wet labs by guiding experimental design, interpreting results, and reducing resource use. They enable rapid hypothesis testing, exploration of large parameter spaces, and in silico screening for drug candidates or new materials, often accelerating discovery cycles and reducing costs when used in conjunction with physical experiments.

Applications span multiple fields, including drug discovery, materials science, physics and chemistry, genomics, epidemiology, climate modeling, and engineering. The rise of big data and AI has expanded the scope of dry lab work, sometimes merging with data-intensive wet lab workflows, such as automated data collection followed by computational analysis.

Key considerations in dry lab work include ensuring reproducibility and validation of models, maintaining data quality and provenance, addressing model interpretability, and promoting transparent methodologies. Training typically emphasizes statistical literacy, programming, and domain knowledge to complement theoretical and practical expertise.