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Quantitative Structure-Activity (mt-QSAR) techniques may become an important tool for prediction of cytotoxicity and High-throughput Screening (HTS) of drugs to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and Linear Discriminant(More)
In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified(More)
The application of 3D-MEDNEs as a novel alternative technique to reduce the use of animal experimentation in toxicology in the early stages of medicinal chemistry research has been extended from agranulocytosis to chemically induced eosinophilia. Firstly, a heterogeneous series of organic compounds, which are classified either as eosinophilia inductors or(More)
Helminth infections are a medical problem in the world nowadays. In this paper a novel atom-level chemical descriptor has been applied to estimate the anthelmintic activity. Total and local linear indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like(More)
Multiplexed biological assays provide multiple measurements of cellular parameters in the same test. In this work, we have trained and tested an Artificial Neural Network (ANN) model for the first time, in order to perform a multiplexing prediction of drugs effect on macrophage populations. In so doing, we have used the TOPS-MODE approach to calculate drug(More)
Entropy measures are universal parameters useful to codify biologically-relevant information in many systems. In our previous work, (Gonzalez-Diaz, H., et al. Chem. Res. Toxicol. 2003, 16, 1318-1327), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs) to study the quantitative(More)
G1 (1-[5-bromofur-2-il]-2-bromo-2-nitroethene) is a novel antimicrobial compound developed in Cuba with reported broadspectrum activity against Gram-positive and -negative bacteria, yeasts and fungi. A compound of this nature may have considerable therapeutic potential. We tested the in vitro activity of this novel compound against 3595 organisms using(More)
Furvina®, also denominated G1 (MW 297), is a synthetic nitrovinylfuran [2-bromo-5-(2-bromo-2-nitrovinyl)-furan] antibiotic with a broad antimicrobial spectrum. An ointment (Dermofural®) containing G1 as the only active principle is currently marketed in Cuba and successfully used to treat dermatological infections. Here we describe the molecular target and(More)
A novel approach to molecular negentropy from the point of view of Markov models is introduced. Stochastic negentropies (MEDNEs) are used to develop a linear discriminant analysis. The discriminant analysis produced a set of two discriminant functions, which gave rise to a very good separation of 93.38% of 151 chemicals (training series) into two groups.(More)
In this work we report a QSAR model that discriminates between chemically heterogeneous classes of anticoccidial and non-anticoccidial compounds. For this purpose we used the Markovian Chemicals in silico Design (MARCH-INSIDE) approach J. Mol. Mod.2002, 8, 237-245; J. Mol. Mod.2003, 9, 395-407]. Linear discriminant analysis allowed us to fit the(More)