Reusable scripts for end-to-end pharmaceutical R&D.
PharmaInformatics is a curated collection of Python/R/Java scripts covering the full drug development lifecycle—from target discovery to commercial strategy. Designed for computational biologists, data scientists, and pharma innovators.
This project covers seven key stages of pharma R&D, including Target ID, Hit Discovery, Preclinical, Clinical, Operation (IT, HR, Finance), Manufacturing, and Commercial.
PharmaInformatics welcomes production-ready scripts in Python, R, Bash, Java, or JavaScript that address real pharmaceutical R&D challenges across six key domains: Target Discovery, Hit Discovery, Preclinical, Clinical, Manufacturing, and Commercial. All contributions must be parameter-driven with no local file dependencies—scripts should accept all inputs via command-line arguments and source data from APIs, databases, or generate it programmatically. We prioritize tools that automate manual processes, integrate multiple data sources, standardize industry calculations, and enable reproducible research workflows, such as literature mining tools extracting structured data from PubMed, QSAR models predicting drug properties from molecular structures, clinical trial simulators modeling patient outcomes, regulatory compliance checkers for submission preparation, market analysis tools integrating competitive intelligence, and manufacturing optimization algorithms for process improvement. Each script must include robust error handling, comprehensive documentation with usage examples, cross-platform compatibility, and standard output formats (CSV, JSON, PNG/SVG), while maintaining professional code quality with meaningful function documentation, input validation, and adherence to open-source licensing standards that advance pharmaceutical innovation through shared, reusable computational tools.
Contributing is simple! Just follow these steps:
- Fork the repository.
- Add a new script—it can be in R, Python, Bash, Java, or any other language.
- Place your script in the
src/folder. - Make sure your script can be executed from the command line using a format like:
./src/pubmed_target_search.py -d "Alzheimer's disease" -n 100 -o ./output/alzheimer_targets.csv