Development of machine learning classifiers to predict compound activity on prostate cancer cell lines.

ABSTRACT: Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective therapeutics is urgently needed. On these premises, we developed a series of machine learning models, based on compounds with reported highly homogeneous cell-based antiproliferative assay data, able to predict the activity of ligands towards the PC-3 and DU-145 prostate cancer cell lines. The data employed in the development of the computational models was finely-tuned according to a series of thresholds for the classification of active/inactive compounds, to the number of features to be implemente

SUBMITTER: Bonanni D 

PROVIDER: S-EPMC9641853 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature