The proposed machine learning based approach could predict the Log (IC50) values of small molecule Protein-Protein Interaction Modulators (PPIMs).

Performance Measurement

Pearson Correlation Coefficient (PCC) and Spearman’s Rank Correlation (SRC) measurement have been used for evalution: 5-fold cross validation training/testing and blind sets to any types of PPIs and two specific PPIs:Mdm2/P53 and Bcl2/Bak. The SVM RBF, polynomial and linear kernel with different parameters including regularization parameter and error parameters were used for building up regression models.

Datasets

Results and brief description

We compiled 1401 of small chemical for 8 different target PPIs like Mdm2/P53, Bcl2/Bak, c-Myc/Max, BRD2/AcK, Cyclophilin, MLL, Rad51, and TNFa. We developed SVM RBF models using of 1200 small chemicals for any type of PPIs in 5-fold cross-validation technique. The SVM RBF model achieved better Pearson correlation coefficient (PCC) value of 0.73 than specific models for Mdm2/P53 and Bcl2/Bak. The best model was also evaluated on the blind dataset that contains random 201 small chemicals and we found better Pearson correlation coefficient values of 0.63. In summary, the model can easily predict the IC50 values of small chemicals targeting any PPIs. The model was not specific to any particular PPIs.

However, in case of specific Mdm2/P53 dataset, we found that the best SVM RBF model for Mdm2/P53 get 0.58 as Max. Pearson correlation coefficient (PCC)

Similarly, in case of specific Bcl2/Bak dataset, we found the best SVM RBF model for Bcl2/Bak get 0.54 as Max Pearson correlation coefficient (PCC).

The small chemicals PPIMs which are already pursued the clinically trials

Result interpretation of PPIM-IC50pred server