RT Journal Article
SR Electronic
T1 Machine learning enables accurate and rapid prediction of active molecules against breast cancer cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.06.459060
DO 10.1101/2021.09.06.459060
A1 Shuyun He
A1 Duancheng Zhao
A1 Yanle Ling
A1 Hanxuan Cai
A1 Yike Cai
A1 Jiquan Zhang
A1 Ling Wang
YR 2021
UL http://biorxiv.org/content/early/2021/09/06/2021.09.06.459060.abstract
AB Summary 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 networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689–0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC and its local version software were developed to predict potential anti-BC agents.Availability ChemBC webserver is available at http://chembc.idruglab.cn/ and its local version Python software is maintained at a GitHub repository (https://github.com/idruglab/ChemBC).Contact zjqgmc{at}163.com or lingwang{at}scut.edu.cnSupplementary information Supplementary data are available at Bioinformatics online.Competing Interest StatementThe authors have declared no competing interest.