Search results for "Data type"
showing 10 items of 1183 documents
<strong>Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction </strong>
2015
The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures. .
Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices
2012
In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 fo…
A topological substructural approach for the prediction of P-glycoprotein substrates
2006
A topological substructural molecular design approach (TOPS-MODE) has been used to predict whether a given compound is a P-glycoprotein (P-gp) substrate or not. A linear discriminant model was developed to classify a data set of 163 compounds as substrates or nonsubstrates (91 substrates and 72 nonsubstrates). The final model fit the data with sensitivity of 82.42% and specificity of 79.17%, for a final accuracy of 80.98%. The model was validated through the use of an external validation set (40 compounds, 22 substrates and 18 nonsubstrates) with a 77.50% of prediction accuracy; fivefold full cross-validation (removing 40 compounds in each cycle, 80.50% of good prediction) and the predictio…
Combined use of PCA and QSAR/QSPR to predict the drugs mechanism of action. An application to the NCI ACAM Database
2009
During the years the National Cancer Institute (NCI) accumulated an enormous amount of information through the application of a complex protocol of drugs screening involving several tumor cell lines, grouped into panels according to the disease class. The Anti-cancer Agent Mechanism (ACAM) database is a set of 122 compounds with anti-cancer activity and a reasonably well known mechanism of action, for which are available drug screening data that measure their ability to inhibit growth of a panel of 60 human tumor lines, explicitly designed as a training set for neural network and multivariate analysis. The aim of this work is to adapt a methodology (previously developed for the analysis of …
QSAR models for tyrosinase inhibitory activity description applying modern statistical classification techniques: A comparative study
2010
Abstract Cluster analysis (CA), Linear and Quadratic Discriminant Analysis (L(Q)DA), Binary Logistic Regression (BLR) and Classification Tree (CT) are applied on two datasets for description of tyrosinase inhibitory activity from molecular structures. The first set included 701 tyrosinase inhibitors (TI) that are used for performance of inhibitory and non-inhibitory activity and the second one is for potency estimation of active compounds. 2D TOMOCOMD-CARDD atom-based quadratic indices are computed as molecular descriptors. CA is used to “rational” design of training (TS) and prediction set (PS) but it shows of not being adequate as classification technique. On the first data, the overall a…
Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening
2014
Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of …
Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity.
2005
The TOpological MOlecular COMputer Design (TOMOCOMD-CARDD) approach has been introduced for the classification and design of antimicrobial agents using computer-aided molecular design. For this propose, atom, atom-type, and total quadratic indices have been generalized to codify chemical structure information. In this sense, stochastic quadratic indices have been introduced for the description of the molecular structure. These stochastic fingerprints are based on a simple model for the intramolecular movement of all valence-bond electrons. In this work, a complete data set containing 1006 antimicrobial agents is collected and presented. Two structure-based antibacterial activity classificat…
Applications of Bond-Based 3D-Chiral Quadratic Indices in QSAR Studies Related to Central Chirality Codification
2009
The concept of bond-based quadratic indices is generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design, we have modeled several well-known data sets. In particularly, Cramer's steroid data set has become a benchmark for the assessment of novel QSAR methods. This data set has been used by several researchers using 3D-QSAR approaches. Therefore, it is selected by us for the shake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design, we model the angiotensin-converting enzyme inhibitory activity o…
Virtual darwinian drug design: QSAR inverse problem, virtual combinatorial chemistry, and computational screening.
2001
The generation of diversity and its further selection by an external system is a common mechanism for the evolution of the living species and for the current drug design methods. This assumption allows us to label the methods based on generation and selection of molecular diversity as "Darwinian" ones, and to distinguish them from the structure-based, structure-modulation approaches. An example of a Darwinian method is the inverse QSAR. It consists of the computational generation of candidate chemical structures and their selection according to a previously established QSAR model. New trends in the field of combinatorial chemical syntheses comprise the concepts of virtual combinatorial synt…
Supersymmetry parameter analysis: SPA convention and project.
2005
18 páginas, 6 figuras, 12 tablas.-- et al.