Search results for "Discriminant"
showing 10 items of 344 documents
Identification In Silico and In Vitro of Novel Trypanosomicidal Drug-Like Compounds
2012
Atom-based bilinear indices and linear discriminant analysis are used to discover novel trypanosomicidal compounds. The obtained linear discriminant analysis-based quantitative structure–activity relationship models, using non-stochastic and stochastic indices, provide accuracies of 89.02% (85.11%) and 89.60% (88.30%) of the chemicals in the training (test) sets, respectively. Later, both models were applied to the virtual screening of 18 in-house synthesized compounds to find new pro-lead antitrypanosomal agents. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Predictions agree with experimental results to a great extent (16/18…
Prediction of tyrosinase inhibition activity using atom-based bilinear indices.
2007
A set of novel atom-based molecular fingerprints is proposed based on a bilinear map similar to that defined in linear algebra. These molecular descriptors (MDs) are proposed as a new means of molecular parametrization easily calculated from 2D molecular information. The nonstochastic and stochastic molecular indices match molecular structure provided by molecular topology by using the kth nonstochastic and stochastic graph-theoretical electronic-density matrices, M(k) and S(k), respectively. Thus, the kth nonstochastic and stochastic bilinear indices are calculated using M(k) and S(k) as matrix operators of bilinear transformations. Chemical information is coded by using different pair com…
New agents active against Mycobacterium avium complex selected by molecular topology: a virtual screening method
2003
Objectives: In order to select new drugs and to predict their in vitro activity against Mycobacterium avium complex (MAC), new quantitative structure-activity relationship (QSAR) models were developed. Methods: The activities against MAC of 29 structurally heterogeneous drugs were examined by means of linear discriminant analysis (LDA) and multilinear regression analysis (MLRA) by using topological indices (TI) as structural descriptors. In vitro antimycobacterial activities were determined by a broth microdilution method with 7H9 medium. Results: The topological model obtained successfully classifies over 80% of compounds as active or inactive; consequently, it was applied in the search fo…
New Analgesics Designed by Molecular Topology
1996
Molecular topology has been applied to the design of new analgesic drugs, utilizing linear discriminant analysis and connectivity functions using different topological descriptors. Of a total of 26 compounds selected, 17 showed analgesic activity. The following stood out particularly, showing analgesic values greater than 75% regarding ASA (acetylsalicylic acid), the reference drug: 2-(1-propenyl)phenol, 2′4′ dimethylacetophenone, p-chlorobenzohydrazide, 1-(p-chlorophenyl) propanol and 4-benzoyl-3-methyl-1-phenyl-2-pyrazolin-5-one. The usefulness of the design method has been demonstrated in the search of new chemical structures having analgesic effects, some of which could become “lead dru…
A topological sub-structural approach for predicting human intestinal absorption of drugs.
2004
The human intestinal absorption (HIA) of drugs was studied using a topological sub-structural approach (TOPS-MODE). The drugs were divided into three classes according to reported cutoff values for HIA. "Poor" absorption was defined as HIAor =30%, "high" absorption as HIAor =80%, whereas "moderate" absorption was defined between these two values (30%HIA79%). Two linear discriminant analyses were carried out on a training set of 82 compounds. The percentages of correct classification, for both models, were 89.02%. The predictive power of the models were validated by three test: a leave-one-out cross validation procedure (88.9% and 87.9%), an external prediction set of 127 drugs (92.9% and 80…
Bond-Based 2D Quadratic Fingerprints in QSAR Studies: Virtual and In vitro Tyrosinase Inhibitory Activity Elucidation
2010
In this report, we show the results of quantitative structure–activity relationship (QSAR) studies of tyrosinase inhibitory activity, by using the bond-based quadratic indices as molecular descriptors (MDs) and linear discriminant analysis (LDA), to generate discriminant functions to predict the anti-tyrosinase activity. The best two models [Eqs (6) and (12)] out of the total 12 QSAR models developed here show accuracies of 93.51% and 91.21%, as well as high Matthews correlation coefficients (C) of 0.86 and 0.82, respectively, in the training set. The validation external series depicts values of 90.00% and 89.44% for these best two equations (6) and (12), respectively. Afterwards, a second …
Search forWHassociated production inpp¯collisions ats=1.96 TeV
2012
This report describes a search for associated production of W and Higgs bosons based on data corresponding to an integrated luminosity of L approximate to 5.3 fb(-1) collected with the D0 detector at the Fermilab Tevatron p (p) over bar Collider. Events containing a W -> l nu candidate (with l corresponding to e or mu) are selected in association with two or three reconstructed jets. One or two of the jets are required to be consistent with having evolved from a b quark. A multivariate discriminant technique is used to improve the separation of signal and backgrounds. Expected and observed upper limits are obtained for the product of the WH production cross section and branching ratios and …
Principal polynomial analysis for remote sensing data processing
2011
Inspired by the concept of Principal Curves, in this paper, we define Principal Polynomials as a non-linear generalization of Principal Components to overcome the conditional mean independence restriction of PCA. Principal Polynomials deform the straight Principal Components by minimizing the regression error (or variance) in the corresponding orthogonal subspaces. We propose to use a projection on a series of these polynomials to set a new nonlinear data representation: the Principal Polynomial Analysis (PPA). We prove that the dimensionality reduction error in PPA is always lower than in PCA. Lower truncation error and increased independence suggest that unsupervised PPA features can be b…
Non Linear Fitting Methods for Machine Learning
2017
This manuscript presents an analysis of numerical fitting methods used for solving classification problems as discriminant functions in machine learning. Non linear polynomial, exponential, and trigonometric models are mathematically deduced and discussed. Analysis about their pros and cons, and their mathematical modelling are made on what method to chose for what type of highly non linear multi-dimension problems are more suitable to be solved. In this study only deterministic models with analytic solutions are involved, or parameters calculation by numeric methods, which the complete model can subsequently be treated as a theoretical model. Models deduction are summarised and presented a…
Random forests, a novel approach for discrimination of fish populations using parasites as biological tags.
2008
Due to the complexity of host-parasite relationships, discrimination between fish populations using parasites as biological tags is difficult. This study introduces, to our knowledge for the first time, random forests (RF) as a new modelling technique in the application of parasite community data as biological markers for population assignment of fish. This novel approach is applied to a dataset with a complex structure comprising 763 parasite infracommunities in population samples of Atlantic cod, Gadus morhua, from the spawning/feeding areas in five regions in the North East Atlantic (Baltic, Celtic, Irish and North seas and Icelandic waters). The learning behaviour of RF is evaluated in …