Search results for "Algorithm"
showing 10 items of 4887 documents
Artificial neural network applied to prediction of fluorquinolone antibacterial activity by topological methods.
2000
A new topological method that makes it possible to predict the properties of molecules on the basis of their chemical structures is applied in the present study to quinolone antimicrobial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. This makes it possible to determine the minimal inhibitory concentration (MIC) of quinolones. Analysis of the results shows that the experimental and calculated values are highly similar. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried …
Application of molecular topology to the prediction of potency and selection of novel insecticides active against malaria vectors
2005
Abstract A study on the basis of molecular topology has been carried out to predict the potency of insecticides active against malaria vectors (Culex) as well as to select novel compounds potentially active on those vectors. The results, performed over two sets of compounds, namely hormone-like and ‘common’ or wide-spectra insecticides, demonstrate that the adequate combination of topological charge indices and simple topological-geometric indices, yield very good results in both, the prediction of potency and the selection of new insecticides. Further development should be addressed in the future; however, the achievement described here is extremely encouraging.
Application of the modelling power approach to variable subset selection for GA-PLS QSAR models
2007
A previously developed function, the Modelling Power Plot, has been applied to QSARs developed using partial least squares (PLS) following variable selection from a genetic algorithm (GA). Modelling power (Mp) integrates the predictive and descriptive capabilities of a QSAR. With regard to QSARs for narcotic toxic potency, Mp was able to guide the optimal selection of variables using a GA. The results emphasise the importance of Mp to assess the success of the variable selection and that techniques such as PLS are more robust following variable selection.
Predicting antitrichomonal activity: A computational screening using atom-based bilinear indices and experimental proofs
2006
Existing Trichomonas vaginalis therapies are out of reach for most trichomoniasis people in developing countries and, where available, they are limited by their toxicity (mainly in pregnant women) and their cost. New antitrichomonal agents are needed to combat emerging metronidazole-resistant trichomoniasis and reduce the side effects associated with currently available drugs. Toward this end, atom-based bilinear indices, a new TOMOCOMD-CARDD molecular descriptor, and linear discriminant analysis (LDA) were used to discover novel, potent, and non-toxic lead trichomonacidal chemicals. Two discriminant functions were obtained with the use of non-stochastic and stochastic atom-type bilinear in…
QSAR multi-target in drug discovery: a review.
2013
The main purpose of the present review is to summarize the most significant works up to date in the field of multi-target QSAR (mt-QSAR), in order to emphasize the importance that this technique has acquired over the last decade. Unlike traditional QSAR techniques, mt-QSAR permits to calculate the probability of activity of a given compound against different biological or pharmacological targets. In simple terms, a single equation for multiple outputs. To emphasize more the importance of the mt-QSAR in the field of drug discovery, we also present a novel mt-QSAR model, made on purpose by our research group, for the prediction of the susceptibility of Gram + and Gram - anaerobic bacteria.
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…
Event-based criteria in GT-STAF information indices: theory, exploratory diversity analysis and QSPR applications
2012
Versatile event-based approaches for the definition of novel information theory-based indices (IFIs) are presented. An event in this context is the criterion followed in the "discovery" of molecular substructures, which in turn serve as basis for the construction of the generalized incidence and relations frequency matrices, Q and F, respectively. From the resultant F, Shannon's, mutual, conditional and joint entropy-based IFIs are computed. In previous reports, an event named connected subgraphs was presented. The present study is an extension of this notion, in which we introduce other events, namely: terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subg…
Predictive modeling of aryl hydrocarbon receptor (AhR) agonism
2020
Abstract The aryl hydrocarbon receptor (AhR) plays a key role in the regulation of gene expression in metabolic machinery and detoxification systems. In the recent years, this receptor has attracted interest as a therapeutic target for immunological, oncogenic and inflammatory conditions. In the present report, in silico and in vitro approaches were combined to study the activation of the AhR. To this end, a large database of chemical compounds with known AhR agonistic activity was employed to build 5 classifiers based on the Adaboost (AdB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms, respectively. The built classifier…
Vanilloid Derivatives as Tyrosinase Inhibitors Driven by Virtual Screening-Based QSAR Models
2010
A number of vanilloids have been tested as tyrosinase inhibitors using Ligand-Based Virtual Screening (LBVS) driven by QSAR (Quantitative Structure-Activity Relationship) models as the multi-agent classification system. A total of 81 models were used to screen this family. Then, a preliminary cluster analysis of the selected chemicals was carried out based on their bioactivity to detect possible similar substructural features among these compounds and the active database used in the QSAR model construction. The compounds identified were tested in vitro to corroborate the results obtained in silico. Among them, two chemicals, isovanillin (K(M) (app) = 1.08 mM) near to kojic acid (reference d…
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…