Welcome to PyTrial documentation!

PyTrial is an easy-to-use Python package for a series of AI for drug development tasks. Clinical trial is the major step of the drug development process, where phase I, II, III, & IV trials are performed to comprehensively evaluate the efficacy and safety of a new drug.

PyTrial is featured for the following tasks and we are adding more!

  • Patient outcome prediction: predict the patient outcomes using tabular or sequential patient data.

  • Trial site selection: pick the best trial sites considering multiple objectives.

  • Trial outcome prediction: predict the trial outcomes using tabular or sequential trial data.

  • Patient-trial matching: match trials’ elibility criteria to patients’ EHRs for participant recruitment of clinical trials.

  • Trial similarity search: encode and retrieve similar clinical trials based on trial design documents.

  • Trial data simulation: generate synthetic EHRs or trial patient records in table or sequence.


The demonstration of clinical trial tasks supported by PyTrial.

The package is developed based on our extensive research in AI for clinical trials over several years. Here is a list of select papers.


The list of select papers that PyTrial is based on.

If you find this package useful, please consider citing it in your scientific publications:

  title={PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications},
  author={Wang, Zifeng and Theodorou, Brandon and Fu, Tianfan and Xiao, Cao and Sun, Jimeng},
  journal={arXiv preprint arXiv:2306.04018},