Publications by authors named "Francisco J Prado-Prado"

In nature, pathogenic parasite species with different susceptibility patterns of antiparasitic drugs abound. In this sense, natural products derived from plants are a potency for drugs with potential antiparasitic activity. Unfortunately, there are many metabolites and studying all of them would be costly in terms of money and resources.

View Article and Find Full Text PDF

Cecropia obtusifolia bertol is medicinal specie used in the treatment of diabetes mellitus and hypertension and it has scientific studies that support the traditional use. However, it is required to understand the influence of drying temperature on the yield and pharmacological activity. Drying rate, extraction efficiency, changes in the UV-Vis spectrum and estimating chlorophylls were stimulated with the increasing temperature.

View Article and Find Full Text PDF

In a multi-target complex network, the links (L(ij)) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K(i), K(m), IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database.

View Article and Find Full Text PDF

Topological Indices (TIs) are numerical parameters useful to carry out Quantitative Structure-Property Relationships (QSPR) analysis and predict the effect of perturbations in many types of Complex Networks. This work, focuses on a very powerful class of TIs called Galvez charge transfer indices. First, we review the classic concept and some applications of these indices.

View Article and Find Full Text PDF

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 Analysis (LDA) function. This model correctly classifies 8258 out of 9000 (Accuracy = 91.

View Article and Find Full Text PDF

Entropy measures are universal parameters useful to codify biologically-relevant information in many systems. In our previous work, (Gonzalez-Diaz, H., et al.

View Article and Find Full Text PDF

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 molecular descriptors and the software STATISTICA to seek different ANN models such as: Linear Neural Network (LNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN) and Multi-Layer Perceptrons (MLP).

View Article and Find Full Text PDF

Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure.

View Article and Find Full Text PDF

Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure).

View Article and Find Full Text PDF

There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance.

View Article and Find Full Text PDF

Trypanosoma brucei causes African trypanosomiasis in humans (HAT or African sleeping sickness) and Nagana in cattle. The disease threatens over 60 million people and uncounted numbers of cattle in 36 countries of sub-Saharan Africa and has a devastating impact on human health and the economy. On the other hand, Trypanosoma cruzi is responsible in South America for Chagas disease, which can cause acute illness and death, especially in young children.

View Article and Find Full Text PDF

The development of methods that can predict the metal-mediated biological activity based only on the 3D structure of metal-unbound proteins has become a goal of major importance. This work is dedicated to the amino terminal Cu(II)- and Ni(II)-binding (ATCUN) motifs that participate in the DNA cleavage and have antitumor activity. We have calculated herein, for the first time, the 3D electrostatic spectral moments for 415 different proteins, including 133 potential ATCUN antitumor proteins.

View Article and Find Full Text PDF

The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure.

View Article and Find Full Text PDF

There are many of pathogen bacteria species which very different susceptibility profile to different antibacterial drugs. There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Bacteria Pairs (DBPs) of affinity/non-affinity drugs with similar/dissimilar bacteria and represented it as a large network, which may be used to identify drugs that can act on bacteria.

View Article and Find Full Text PDF

Since the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort.

View Article and Find Full Text PDF

The number of protein and peptide structures included in Protein Data Bank (PDB) and Gen Bank without functional annotation has increased. Consequently, there is a high demand for theoretical models to predict these functions. Here, we trained and validated, with an external set, a Markov Chain Model (MCM) that classifies proteins by their possible mechanism of action according to Enzyme Classification (EC) number.

View Article and Find Full Text PDF

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major).

View Article and Find Full Text PDF

The most important limitation of antifungal QSAR models is that they predict the biological activity of drugs against only one fungal species. This is determined due the fact that most of the up-to-date reported molecular descriptors encode only information about the molecular structure. Consequently, predicting the probability with which a drug is active against different fungal species with a single unifying model is a goal of major importance.

View Article and Find Full Text PDF

In the previous work, we reported a multitarget Quantitative Structure-Activity Relationship (mt-QSAR) model to predict drug activity against different fungal species. This mt-QSAR allowed us to construct a drug-drug multispecies Complex Network (msCN) to investigate drug-drug similarity (González-Díaz and Prado-Prado, J Comput Chem 2008, 29, 656). However, important methodological points remained unclear, such as follows: (1) the accuracy of the methods when applied to other problems; (2) the effect of the distance type used to construct the msCN; (3) how to perform the inverse procedure to study species-species similarity with multidrug resistance CNs (mdrCN); and (4) the implications and necessary steps to perform a substructural Triadic Census Analysis (TCA) of the msCN.

View Article and Find Full Text PDF

One limitation of almost all antiviral Quantitative Structure-Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug-drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases.

View Article and Find Full Text PDF

Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important.

View Article and Find Full Text PDF

There are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantially increases the potentialities of this kind of networks, avoiding time and resource-consuming experiments.

View Article and Find Full Text PDF

There are many different kinds of pathogenic bacteria species with very different susceptibility profiles to different antibacterial drugs. One limitation of QSAR models is that they consider the biological activity of drugs against only one species of bacteria. In a previous paper, we developed a unified Markov model to describe the biological activity of different drugs tested in the literature against some antimicrobial species.

View Article and Find Full Text PDF

Most of up-to-date reported molecular descriptors encode only information about the molecular structure. In previous papers, we have extended stochastic descriptors to encode additional information such as target site, partition system, or biological species [Bioorg. Med.

View Article and Find Full Text PDF