Preface

The research notebook contains the code of the machine learning algorithms used to generate the results in the paper “Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery.” The research involves a super learner-based methodology to improve multifunctional peptide engineering. The aim is to impart high melanin binding, high cell-penetration, and low cytotoxicity to ocular drugs through peptide-drug conjugation, with the ultimate goal of enhancing the sustained delivery of the drug to maintain the therapeutic level in the eye for a prolonged period.

Citation:

Chou RT, et al. Supplementary materials for machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery. https://doi.org/10.13016/0jck-hnnv, (2023).

@misc{https://doi.org/10.13016/0jck-hnnv,
  doi = {10.13016/0JCK-HNNV},
  url = {https://drum.lib.umd.edu/handle/1903/29529},
  author = {Chou,  Renee Ti and Hsueh,  Henry T. and Rai,  Usha and Liyanage,  Wathsala and Kim,  Yoo Chun and Appell,  Matthew B. and Pejavar,  Jahnavi and Leo,  Kirby T. and Davison,  Charlotte and Kolodziejski,  Patricia and Mozzer,  Ann and Kwon,  HyeYoung and Sista,  Maanasa and Anders,  Nicole M. and Hemingway,  Avelina and Rompicharla,  Sri Vishnu Kiran and Edwards,  Malia and Pitha,  Ian and Hanes,  Justin and Cummings,  Michael P. and Ensign,  Laura M.},
  keywords = {machine learning,  drug delivery},
  language = {en},
  title = {Supplementary materials for machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery},
  publisher = {Digital Repository at the University of Maryland},
  year = {2023}
}

Creative Commons License
The notebook is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.