Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells.

ABSTRACT: Breast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naïve Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional netwo

SUBMITTER: He S 

PROVIDER: S-EPMC8719637 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature