Study On Predictions Of Drug-target Interactions And Drug Combinations

Drug discovery and development have raised widespread attention in the past twodecades and the target-orienented drug pharmacology has also achived great success.Identifying therapeutic targets and seeking specific drugs for targets, which are thefocal point for pharmaceutical enterprises and laboratory research, have alreadyachieved great progress and made indelible contributions to human health. However,in recent year, new drug development rate slows down and the cost of researchcontinues to rise, mainly due to two reasons: first is that vast screening of drugcandidates in early state still relies on time and labor consuming experimental means,while in later stage the unsatisfactory efficacy or side-effects of the drug may lead tofailures; second is that as most human diseases are complex disease induced by manyfactors, and the biological system has a certain degree of redundancy and robustness,the interference on single target by single drug can not alter the system phenotype.With the development of all kinds of omics, the accumulation of large amounts ofbiological data leads to the continuous expansion of biological database. Thedevelopment of bioinformatics and computational biology provides an effectivemeans to solve difficulties in drug development. Especially in the early stages of drugdevelopment, virtual screening method provides an efficient and high-throughputtechnique, which plays an important role in saving cost and narrowing down theresearch scope. It is at just the right time to use in silico methods for the integration ofa variety of data sources, data mining of the underlying associations, and screening ofreliable drug-target interactions and effective drug combinations.Based on the public database resources, this paper designs different computationalmodels, and verifies their effectiveness aiming at the two hot issues that concern in the medical field, drug-target interaction and drug combination prediction. The mainresearch work of this paper includes the following three parts:1. A chemical similarity ensemble model is established to explore the protein-ligandinteractions from public databases in a large scale. This research covers a total of53092ligand and14732human proteins: the selected proteins contains not only afew known drug targets, but also those with rich ligand information (more than5ligands each); the selection of ligands is not limited to a few commercial drugs,but contains drugs, small molecular compounds, ions et al that can be used asprotein ligands. Our reasearch has greatly enriched the application scope ofchemical similarity ensemble method. Using two different ligand fingerprintsGpiDAPH3and MACCS key, the areas under the ROC curves (AUC) achieve0.6608and0.8344respectively. It can be found that, the similarity ensemblemodel using MACCS key fingerprint still maintains a good prediction capability,showing strong extensibility. Later, the study of seeking protein targets forTraditional Chinese Medicine composition further illustrates that the chemicalsimilarity ensemble method has a certain validity to predict new drug-targetinteractions.2. A support vector machine (SVM) model based on the chemical-protein bindingsfrom STITCH is developed. New features have been built from ligand structurespace and ligand-protein networks and then chosen as the the input parameters forSVM model.332feature vectors are constructed from both ligand fingerprint andprotein-ligand interactions, called as chemical preference feature vectors. Thismodel shows good ability in predicting protein-ligand interactions, whichoutperforms the state-of-the-art method based on ligand similarity. The resultedAUC for5-fold cross validation and independent test reaches as high as0.9914and0.9878, respectively, achiving a very high accuracy of prediction.Furthermore, in order to simplify the model,182distinct features in pairs havebeen chosen to rebuild a new model which still shows similar outcome as the one built on the whole332features. Then, this refined model is used to search for thepotential D-amino acid oxidase (DAO) inhibitors out of STITCH database andthe predicted results are finally verified by our wet experiments. Out of10candidates obtained, seven DAO inhibitors have been verified, in which four arenewly found in the present study, and one may have a new application in therapyof psychiatric disorders other than being an antineoplastic agent. Obviously, themodel in this paper possesses abilities for high-throughput new drug and targetdiscovery in a timely manner.3. A new calculation method is proposed by integrating gene chip data andsub-network under the drug effect, as well as pathway information available, tobuild a machine learning model, for the prediction of drug combination. Firstlygene expression data of single drug is used to forecast gene expression variationratio of drug combinations. The weight of existing PPI network is definedaccording to gene expression ratios before and after the treatment, and theoptimal drug sub-network is identified by jActiveModules. Genes in the optimalsub-network is thought to be the response of cell system to drug interference. Thefrequencies of the genes in the optimal sub-network by drug combinations andsingle drug alone appearing in different pathways are adopted as the featurevectors to optimize the features and construct the model. Results of the crossvalidation indicate that mean area under the ROC curve reaches0.7941, whichindicates that this model can classify the positive and negative samples of drugcombination very well. A case study in cancer as an instance finds that, amongthe first10forecasted drug combinations, three already exist in the database andtwo others are supported in literature review, further indicating the effectivenessof the model.