Deep Learning Based Anticancer Drug Combination Discovery Through Natural Compounds and Pharmaceutical Drugs

Drug combination therapies have shown effective performance in treating cancer through increased efficacy and circumvention of drug resistance through drug synergy. Two avenues can be used to discover drug combinations: a novel approach that utilizes natural products compared with the textbook approach of utilizing existing chemotherapy drug combinations. Many natural products achieve efficacy due to synergistic interactions between the active ingredients. Therefore, the pharmacophore relationships in herbal compounds which synergize can potentially be applied to chemotherapy drugs to drive combination discovery. Machine learning approaches have been developed to identify drug combinations, especially deep neural networks (DNN), which have achieved state-of-the-art performance in many drug discovery tasks. Here, we developed a drug protein interaction (DPI) prediction DNN, DeepDPI, to employ DPI drug representations and achieved state-of-the-art performance. Two DNNs were also developed to predict novel drug combinations: DeepNPD, which predicts combinations in herbs, and DeepCombo, which predicts synergy in chemotherapy drugs. We used an ensemble architecture enhanced with a novel similarity based weight adjustment (SBWA) approach and both models accurately predicted drug combinations for both known and unknown drugs. Lastly, a screening was conducted using each model where DeepNPD predicted combinations where drugs had similar targets, while DeepCombo predicted combinations where one agent potentiated the other, with both models’ predicted combinations investigated through a network-based analysis and identifying as a synergistic combinations in literature. DeepNPD illustrates how natural products are a novel path where new drug combinations can be discovered.