In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches

With the advent of High Throughput Screening techniques, it is feasible to filter possible leads from a mammoth chemical space that can act against a particular target and inhibit its action. Virtual screening complements the in-vitro assays which are costly and time consuming. This process is used to sort biologically active molecules by utilizing the structural and chemical information of the compounds and the target proteins in order to screen potential hits. Various data mining and machine learning tools utilize Molecular Descriptors through the knowledge discovery process using classifier algorithms that classify the potentially active hits for the drug development process.
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RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2016Prediction of unknown drug target interactions from bioassay data is critical not only for the understanding of various interactions but also crucial for the development of new drugs and repurposing of old ones. Conventional methods for prediction of such interactions can be divided into 2D based and 3D based methods. 3D methods are more CPU expensive and require more manual interpretation whereas 2D methods are actually fast methods like machine learning and similarity search which use chemical fingerprints. One of the problems of using traditional machine learning based method to predict drug-target pairs is that it requires a labeled information of true and false interactions. One of the major problems of supervised learning methods is selection on negative samples. Unknown drug target interactions are regarded as false interactions, which may influence the predictive accuracy of the model. To overcome this problem network based methods has become an effective tool in predicting the drug target interactions overcoming the negative sampling problem. In this dissertation study, I will describe traditional machine learning methods and 3D methods of pharmacophore modeling for drug target prediction and will show how these methods work in a drug discovery scenario. I will then introduce a new framework for drug target prediction based on bipartite networks of drug target relations known as Random Walk with Restart (RWR). RWR integrates various networks including drug– drug similarity networks, protein-protein similarity networks and drug- target interaction networks into a heterogeneous network that is capable of predicting novel drug-target relations. I will describe how chemical features for measuring drug-drug similarity do not affect performance in predicting interactions and further show the performance of RWR using an external dataset from ChEMBL database. I will describe about further implementations of RWR approach into multilayered networks consisting of biological data like diseases, tissue based gene expression data, protein- complexes and metabolic pathways to predict associations between human diseases and metabolic pathways which are very crucial in drug discovery. I have further developed a software tool package netpredictor in R (standalone and the web) for unipartite and bipartite networks and implemented network-based predictive algorithms and network properties for drug-target prediction. This package will be described

In-silico predictive mutagenicity model generation using supervised learning approaches

<p>Abstract</p> <p>Background</p> <p>Experimental screening of chemical compounds for biological activity is a time consuming and expensive practice. <it>In silico</it> predictive models permit inexpensive, rapid “virtual screening” to prioritize selection of compounds for experimental testing. Both experimental and <it>in silico</it> screening can be used to test compounds for desirable or undesirable properties. Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models. In this work, we examined a range of <it>in silico</it> predictive classification models for prediction of mutagenic properties of compounds, including methods such as J48 and SMO which have not previously been widely applied in cheminformatics.</p> <p>Results</p> <p>The Bursi mutagenicity data set containing 4337 compounds (Set 1) and a Benchmark data set of 6512 compounds (Set 2) were taken as input data set in this work. A third data set (Set 3) was prepared by joining up the previous two sets. Classification algorithms including Naïve Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets. Models built using the combined performed better than those developed from the Benchmark data set. Significantly, Random Forest outperformed other classifiers for all the data sets, especially for Set 3 with 89.27% accuracy, 89% precision and ROC of 95.3%. To validate the developed models two external data sets, AID1189 and AID1194, with mutagenicity data were tested showing 62% accuracy with 67% precision and 65% ROC area and 91% accuracy, 91% precision with 96.3% ROC area respectively. A Random Forest model was used on approved drugs from DrugBank and metabolites from the Zinc Database with True Positives rate almost 85% showing the robustness of the model.</p> <p>Conclusion</p> <p>We have created a new mutagenicity benchmark data set with around 8,000 compounds. Our work shows that highly accurate predictive mutagenicity models can be built using machine learning methods based on chemical descriptors and trained using this set, and these models provide a complement to toxicophores based methods. Further, our work supports other recent literature in showing that Random Forest models generally outperform other comparable machine learning methods for this kind of application.</p

Unified processing framework of high-dimensional and overly imbalanced chemical datasets for virtual screening.

Virtual screening in drug discovery involves processing large datasets containing unknown molecules in order to find the ones that are likely to have the desired effects on a biological target, typically a protein receptor or an enzyme. Molecules are thereby classified into active or non-active in relation to the target. Misclassification of molecules in cases such as drug discovery and medical diagnosis is costly, both in time and finances. In the process of discovering a drug, it is mainly the inactive molecules classified as active towards the biological target i.e. false positives that cause a delay in the progress and high late-stage attrition. However, despite the pool of techniques available, the selection of the suitable approach in each situation is still a major challenge. This PhD thesis is designed to develop a pioneering framework which enables the analysis of the virtual screening of chemical compounds datasets in a wide range of settings in a unified fashion. The proposed method provides a better understanding of the dynamics of innovatively combining data processing and classification methods in order to screen massive, potentially high dimensional and overly imbalanced datasets more efficiently