Anti-cancer medicine for a particular patient has been a personal medical goal. Many computational models have been proposed by researchers to predict drug response. But predictive accuracy still remains a challenge. Base on this concept which “Similar cells have similar responses to drugs”, we developed the basic method of matrix factorization method by adding fines to similarity. So that the distance of latent factors to two cell lines or (drug) should be inversely related to similarity. This means that two similar drugs or similar cell lines should have a short distance, whereas two similar cell lines or non-similar drugs should have a large gap with their latent factors.
We proposed a Dual similarity-regularized matrix factorization (DSRMF) model, then generated new data for drug similarity from the two-dimensional three-dimensional chemical structure, which were obtained from the CCLE and GDSC databases. In this research, by using the proposed model, and generating new drug similarity data we achieved the average Pearson correlation coefficient (PCC) about 0.96, and average mean square error (RMSE) Root about 0.30, between the observed value and the predicted value for the cell line response to the drug.
Our analysis in this research showed, using heterogeneous data, has better results, and can be obtained with the proposed model, using other panels’ cancer cell lines, to calculate similarity between cells. Also, by imposing more restrictions on the similarity between cells, we were able to achieve more accurate prediction for the response of the cell line to the anticancer drug.