In complex diseases, multiple cellular mechanisms are often altered in the cell; therefore, treating them with a single drug and focusing on a single target is usually an ineffective strategy.Combination therapy is a promising solution that uses multiple drugs that exhibit better therapeutic efficacy together than the sum of individual drug effects. Combination therapy is of great interest in drug development due to improved efficacy and reduced side effects. This model named as "MatchMaker" is a deep learning-based model that utilizes drug chemical structure and cell line specific expression patterns to predict if two drugs work synergistically. Trained on the largest experimental dataset released to date, results demonstrate that MatchMaker outperforms state-of-the-art approaches in terms of predicting the Loewe score. By pruning the otherwise unmanageable search space, MatchMaker provides a useful tool for prescreening and prioritizing the candidate drug pairs in silico.
Input variables : Drug Pair chemical structure, Cell gene expression
Output Variables : Drug Synergy Score
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Business | : | # of Adverse Events |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : biorxiv.org
| Model Category | : | Public |
| Date Published | : | May, 2020 |
| Healthcare Domain | : | Life Sciences |
| Code | : | github.com |
| Clinical Trials |
| Adverse Drug Events |