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DDI / Drug-Drug Interactions
A feature-based approach to extract DDIs from biomedical text. This approach differs from existing approaches in two ways. First, it partitions candidate DDI pairs into five groups based on their syntactic structures. Second, it applies a set of novel features that is optimized for each group based on the syntactic properties. Results show that the proposed system achieves the best results in terms of F-scores and performance efficiency when compared with the state-of-the-art DDI extraction systems.
Label propagation algorithm
An integrative label propagation framework to predict drug-drug interaction (DDI) by integrating label side effects, off-label side effects, and chemical structures. A systematic comparison of the experimental results shows (1) side effect profiles are more predictive features than chemical structures in DDI prediction. It greatly benefits from the fact that clinical side effects are human phenotypic data obviating translation issues. (2) label propagation algorithm boosted the DDI prediction by considering high-order relationships between drugs. (3) our proposed integrative label propagation algorithm effectively integrated multiple drug properties and outperformed competitors. Furthermore, we applied the proposed algorithm to all known drugs which have one or more side effect profiles and obtained 145,068 predicted DDIs. These predicted DDIs can be leveraged for clinical surveillance and real-world drug discovery.
targetRW
Predicts pharmacodynamic drug-drug interactions (DDI) using interference of signaling propagation through protein-protein interaction (PPI) network. targetRW is a web service with which pharmacodynamic (PD) DDIs for drug pairs can be analyzed. The signaling propagation of drug targets is measured by using a random walk with restart algorithm. The interference of the signaling propagation between drugs for predicting PD DDIs was calculated. This method outperformed previous methods as evaluated by using PD DDIs from DrugBank and KEGG DRUG. In addition, it achieved good performance in various drug categories according to the single/multi-target drugs or Anatomical Therapeutic Chemical codes.
DDI-Fusion
Predicts human DDI (midazolam AUCR) using a proprietary algorithm that takes in vitro CYP interaction data (reversible inhibition, time dependent inhibition and induction) and chemical structure as inputs. DDI-Fusion has several advantages over the regulatory mechanistic static model: (i) it uses a data-driven optimisation approach requiring fewer assumptions, (ii) human Cmax is predicted so no clinical in vivo data are required. Provides early stage filter for directing chemistry and prioritising screening.
HEP Drug Interaction Checker
Offers a resource for healthcare providers, researchers and patients to be able to understand and manage drug-drug interactions. HEP Drug Interaction Checker provide a clinically useful, reliable, comprehensive, up-to-date, evidence-based drug-drug interaction resource, freely available to healthcare workers, patients and researchers. Information presented relates only to known or suspected effects of interacting medications, and is based on relevant data in the public domain. No clinical advice is given or implied, clinicians must exercise their own judgement in relation to the risks and benefits of combining drugs, which depend on factors beyond pharmacokinetic interactions between two drugs.
DDI-Predictor
Makes quantitative predictions of drug exposure e.g. in case of drug-drug interaction (DDI) even if this interaction has not been studied. DDI-Predictor is a website dedicated to quantitative prediction of the impact on drug exposure of drug-drug interactions mediated by cytochromes P450 3A4, 2D6, 2C9, 2C19 and 1A2, as well as genetic polymorphism of these cytochromes, the combined effect of drug interaction and cytochrome polymorphism, cirrhosis, and drug-drug interactions in cirrhotic patients.
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