Drug development is a complex process that aims to test the efficacy and safety of candidate drugs in the human body for regulatory approval via clinical trials. Recently, machine learning has emerged as a vital tool for drug development, offering new opportunities to improve the efficiency and success rates of the process. To facilitate the research and development of artificial intelligence (AI) for drug development, we developed a Python package, namely PyTrial, that implements various clinical trial tasks supported by AI algorithms. To be specific, PyTrial implements 6 essential drug development tasks, including patient outcome prediction, trial site selection, trial outcome prediction, patient-trial matching, trial similarity search, and synthetic data generation. In PyTrial, all tasks are defined by four steps: load data, model definition, model training, and model evaluation, which can be done with a couple of lines of code. In addition, the modular API design allows practitioners to extend the framework to new algorithms and tasks easily. PyTrial is featured for a unified API, detailed documentation, and interactive examples with preprocessed benchmark data for all implemented algorithms. This package can be installed through Python Package Index (PyPI) and is publicly available at https://github.com/RyanWangZf/PyTrial.

开发了一个名为 PyTrial 的 Python 包，实现了支持人工智能算法的各种临床试验任务，包括患者预后预测、试验结果预测、患者 - 试验匹配、试验相似性搜索和合成数据生成。

PyTrial: 人工智能药物研发综合平台