PIPET 수련생을 위한 실습교육자료입니다. (R, NCA, Rmarkdown, data science, 임상시험, 약동학/약력학, 논문발표 등)

An iterative process that uses two machine learning models to generate the best inhibitors for a target protein to help reduce the time and cost of the drug discovery process

Implementing AI in the Clinical Trial Process for Drug Development

PyTrial: A Comprehensive Platform for Artificial Intelligence for Drug Development

가톨릭대학교 의과대학 약리학교실, Department of Pharmacology, College of Medicine http://pharmacology.catholic.ac.kr

Non-compartmental pharmacokinetics analysis for Julia.

Awesome list of the data and AI/ML related projects with direct Life Science Companies participation

benchmark dataset and Deep learning method (Hierarchical Interaction Network, HINT) for clinical trial approval probability prediction, published in Cell Patterns 2022.

R package for translating between drug identifiers using the Chemical Translation Service (CTS)

Description of work done at Merck pharmaceutical company in the summer of 2018 as a Computational Drug Discovery Intern at West Point, PA. Information excludes all proprietary information belonging to Merck & Co.

Exploring off-patent and off-exclusivity drugs without an FDA-approved generic for gener8tor.

“Just because we are not ready for scientific progress does not mean it won’t happen.” - Jennifer A. Doudna

PharmAI are tools for Artificial intelligence in drug discovery and development

Winning solution of the Novartis Data Science and Artificial Intelligence 2019/2020 competition

Development version of ph2rand, an R package for the design of randomized comparative phase II oncology trials

📝 2016, 2017, 2018, 2019 서울아산병원 임상약리학과에 실습 나온 것을 환영합니다. 서브인턴들의 교육을 위해 필요한 자료를 모아두었습니다.