Higher Efficiency In Prediction Of TIBO Activity By Evolutionary Neural Network

The treatment of acquired immunodeficiency syndrome (AIDS) is a challenging medical problem. TIBO is a nonnucleoside reverse transcriptase inhibitor, which binds non-competitively to the hydrophobic pocket on the p66 subunit of RT enzyme. We used a dataset consisting of physicochemical properties and reverse transcriptase inhibitor activities of 88 set of 4,5,6,7-tetrahydro-y-imidazo-[4,5,1-jk][1,4]-x-benzodiazepin-2-(1h)one derivatives that are variously substituted by halogens, alkyl groups. The dataset was taken from the BIOBYTE database at (www.davidhoekman.com). The concentration of the compound leading to 50% effect has been measured and expressed as IC50. The logarithm of the inverse of this parameter has been used as biological end points (log 1/C) in the QSAR studies. The evolutionary neural network (ENN) is a new system for modeling multivariate data. The strengths of ENN’s are that they can extract insignificant predictors, choose the size of the hidden layers and nodes and fine tune the parameters needed in training the network. We have used an ENN to predict the biological activities of Reverse Transcriptase Inhibitors. We have found out that Evolutionary Neural networks are better predictor of activity values than Multiple linear regression and Multilayered Perceptrons. We have calculated the correlation coefficient of each of the methods where we have found ENNs are the best

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

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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Interactome data

Interactome used for doing PPI based analysi

Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links

Background Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download. Results We compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis. Conclusion The rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion

Embedding management knowledge: repertoires and reservoirs

Predictions Results