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The problem of large scale computational drug screening is considered in this paper. A computational framework is developed for microarray profile of drug treatment from the Connectivity Map (cMap) project. The framework is proposed to address the deficiency of cMap prediction. This scheme includes construction of drug Mode-of-Actions (MoA) and subsequently Mode-of-Action (MoA) network, or MoANet...
In this work we propose a hybrid learning machine, combining artificial neural networks (ANNs) and binary decision trees, to predict quantitative structure activity relationships (QSARs). This approach directly uses the structural cues from chemical compounds and has been validated for the two significant prediction problems, viz. regression and classification. For regression analysis we show the...
In this paper, we propose an artificial neural network approach to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARI). In order to accurately describe the structural properties of ARIs, besides the popularly used 2-dimensional (2D) descriptors, we have used 3-dimensional (3D) molecular descriptors which are obtained through the DRAGON software...
We investigated the possibility of gaining information on the mode of action of a set of compounds by means of Gene Ontology (GO) enrichment analysis. To this aim, we developed a new method, based on fuzzy-sets, which is able to compute sets of genes that are consistently differentially expressed when treating cells with the analyzed compounds. Then a Gene Ontology enrichment analysis is performed...
Designing appropriate graphs is a problem frequently occurring in several common applications ranging from designing communication and transportation networks to discovering new drugs. More often than not the graphs to be designed need to satisfy multiple, sometimes conflicting, objectives e.g. total length, cost, complexity or other shape and property limitations. In this paper we present our approach...
In March and April 2009, an outbreak of H1N1 influenza in Mexico led to hundreds of confirmed cases and a number of deaths. The worldwide spread of H1N1 had attracted everyone's attention and arisen an overwhelm fear. Up to now, there is still an urgent need in the solution for ending this light. In this study, a QSAR model of neuraminidase (NA) type 1 (N1) provides an access. The pharmacophore map...
Genetic algorithm (GA) applied to feature selection and model optimization improved the performance of robust mathematical models such as Bayesian-regularized neural networks (BRANNs) and support vector machines (SVMs) on different drug design datasets. The selection of optimum input variables and the adjustment of network weights and biases to optimum values to yield generalizable predictors were...
An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well...
At the state-of-the-art in drug discovery, one of the key challenges is to develop high-throughput screening (HTS) techniques that can measure changes as a continuum of complex phenotypes induced in a target pathogen. Such measurements are crucial in developing therapeutics against diseases like schistosomiasis, trypanosomiasis, and leishmaniasis, which impact millions worldwide. These diseases are...
Combining advanced data mining and biomedical technologies to discovering new drug is an active research field nowadays. In this paper, we collect a herbal compounds for rheum database by searching about 150 prescriptions in ancient herbal document. 255 herbal compounds are included for their combinations to heal rheum. Our aim is to discover potentially new herbal compound in the database. We present...
HCV (Hepatitis C virus) that the NS3 protease and NS5B RNA-dependent RNA polymerase (RbRp) which the enzymes for virtual replication. HCV plays an important role that to cause the chronic and liver diseases. The computer aided drug design (CADD) that is the new method to design the new molecules as like the drugs from the potent compounds. We took the program of Discovery Studio 2.0 and the scoring...
Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as active or inactive with respect to a specific target biological system. This paper presents a comparison of artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT) in an effort to identify potentiators...
This paper presents an empirical evaluation of common vector based methods and some extensions in a particular and difficult domain corresponding to the characterization of pharmacological properties from their chemical structure for automatic drug classification problems. Several classic pattern classification methods have already been applied to this problem with promising results. In particular,...
The prediction of biological activity of a chemical compound from its structural features, representing its physico-chemical properties, plays an important role in drug discovery, design and development. Since the biological data is highly non-linear, the machine-learning techniques have been widely used for modeling it. In the present work, the clustering, genetic algorithm (GA) and artificial neural...
We developed a new method to improve the accuracy of molecular interaction data using a protein-compound affinity matrix calculated by protein-compound docking software. We approximated the protein-compound binding free energy as a linear combination of the raw docking scores of the compound with many different proteins. The coefficients of the linear combination were estimated based on the amino-acid...
The pathway for novel lead drug discovery has many major deficiencies, the most significant of which is the immense size of small molecule diversity space. Methods that increase the search efficiency and/or reduce the size of the search space increase the rate at which useful lead compounds are identified. Artificial neural networks optimized via evolutionary computation provide a cost and time-effective...
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