Synergistic drug combination prediction software tools | Drug discovery data analysis
Recent success in the study of synergistic combinations, such as the use of CHK1 inhibitors in combination with several DNA damaging agents or of the PARP inhibitor olaparib in combination with the PI3K inhibitor BKM120, have generated significant interest in the systematic screening of compound pairs to identify synergistic pairs for combination therapy.
Predict drug combination effects. DIGRE models the drug response dynamics and gene expression changes after individual drug treatments. It takes into account the sequential effect of the treatment, in agreement with many observations that the sequencing of drug treatment matters to patients’ outcomes. The tool uses a mathematical model of the drug response estimates the genomic residual effect.
Permits to conduct synergy predictions and not antagonism. SynGen predicts the full compound-pair ranking is not statistically significant, as the algorithm is not designed to predict compound antagonism. It is able to identify synergistic compound pairs with high sensitivity (56%, P ≤ 0.001). The tool uses the compound pairs that are most complementary in implementing or abrogating Master Regulator (MR) patterns to determine synergistic compound combinations.
Predicts the synergistic potential of the available anticancer drugs. RACS improves drug synergy prediction for guiding experimental searching despite of the unclear synergistic mechanism. It aims to address the limited positive/labelled samples and the large set of unknown/unlabelled combinations. The tool was evaluated using the endocrine receptor (ER)-positive breast cancer cell line MCF7.
Predicts drug combinations targeting multiple signaling modules of cancer-specific networks. DrugComboRanker selects combinations targeting the alternative and complementary signaling modules of disease. It can provide insights into mechanism of actions of drug combinations by mapping the predicted drug targets on the disease signaling network.
Predicts synergy score and selects novel synergistic drug combinations. DeepSynergy is a Deep Learning based method that can learn to differentiate several cancer cell lines and detect distinct drug combinations that have maximal efficacy on a given cell line. This web-based tool uses as input compound and genomic information to determine predictions in a cross-validation setting with external test sets.
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