WMMDCA: Prediction of Drug Responses by Weight-Based Modular Mapping in Cancer Cell Lines

Due to the high consumption of cost and time for experimental verification in clinical trials, drug response prediction by computational models have become important challenges. The existing drug response data in diverse cell lines enable prediction of potential sensitive associations. Here, we propose a weight-based modular mapping method, named as WMMDCA, to predict drug-cell line associations. The method fully considers the effects of drugs' chemical structural feature, and adds modular information into the network projection. Leave-one-out cross-validation was used to evaluate the predictive ability of WMMDCA, which showed the best performance among several state-of-the-art methods in not only the whole dataset but also the major tissue types of cell lines. Literature support of highly ranked potential associations was found manually, demonstrating the effectiveness of WMMDCA on drug response prediction.

Automated summary

Due to the high consumption of cost and time for experimental verification in clinical trials, computational models for drug response prediction have become increasingly important. Our proposed method WMMDCA considers chemical structural features of drugs and adds modular information to predict drug-cell line associations, showing superior performance compared to other state-of-the-art methods.