Machine learning prediction of methionine and tryptophan photooxidation susceptibility
Jared A. Delmar, Eugen Buehler, Ashwin K. Chetty, Agastya Das, Guillermo Miró Quesada, Jihong Wang, Xiaoyu Chen
Abstract
) of 0.511 and root-mean-square error (RMSE) of 10.9%. We further identify important physical, chemical, and formulation parameters that influence photooxidation. Improvement of biopharmaceutical liability predictions will result in better, more stable drugs, increasing development throughput, product quality, and likelihood of clinical success.
Topics & Concepts
TryptophanMethionineRandom forestChemistryComputer scienceMachine learningBiochemistryAmino acidProtein purification and stabilityViral Infectious Diseases and Gene Expression in InsectsBiosimilars and Bioanalytical Methods