Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.

ABSTRACT: Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two limitations, high accuracy and abundance, are often taken together; however, insight into the dataset accuracy limitation of contemporary machine learning algorithms may yield insight into whether non-bench experimental sources of data may be used to generate useful machine learning models where there is a paucity of experimental data. We took highly accurate data across six kinase types, one GPCR, one polymerase, a human protease, and HIV protease, and intentionally introduced error at varying population proportions in the datasets for each tar

SUBMITTER: Kaiser TM 

PROVIDER: S-EPMC6601015 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature