No abstract

The antiviral QSAR models today have an important limitation. Only they predict the biological activity of drugs against only one viral species. This is determined due the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this we use the Markov Chain theory to calculate new multi-target entropy to fit a QSAR model that predict by the first time a ms-QSAR model for 900 drugs tested in the literature against 40 viral species and other 207 drugs no tested in the literature using entropy QSAR. We used Linear Discriminant Analysis (LDA) to classify drugs into two classes as active or non-active against the different tested viral species whose data we processed. The model correctly classifies 31 188 out of 31 213 non-active compounds (99.92%) and 432 out of 434 active compounds (99.54%). Overall training predictability was 98.56%. Validation of the model was carried out by means of external predicting series, the model classifying, thus, 15 588 out of 15 606 non-active compounds and 213 out of 217 active compounds. Overall validation predictability was 98.54%. The present work report the first attempts to calculate within a unify framework probabilities of antiviral drugs against different virus species based on entropy analysis.

Alzheimer's disease (AD) is the most prevalent form of dementia, and current indications show that twenty-nine million people live with AD worldwide, a figure expected rise exponentially over the coming decades. AD is characterize with several pathologies this disease, amyloid plaques, composed of the β-amyloid peptide and γ-amyloid peptide are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that β-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. For this reason, we developed a new QSAR (QSAR) model to discover new drugs. A public database ChEMBL contain Big Data sets of inhibitors of β-secretase. We revised QSAR studies using method of Artificial Neural Network (ANN) in order to understand the essential structural requirement for binding with receptor for β-secretase inhibitors.. .

Hypertension is a multifactorial disease in which blood vessels are extensively exposed to a higher voltage than usual, this tension endures more strain on the heart leading to greater cardiac output to pump blood to the body. Hypertension is classified by the World Health Organization (WHO) as one of the main risk factors for disability and premature death in the world population. WHO has strengthened various health services around the world, listing the groups of basic medicines for high blood pressure such as: angiotensin-converting enzyme inhibitors, thiazide diuretics, beta blockers, long-acting calcium channel blockers, among other groups for drug treatment to the population with this condition. The discovery of new drugs with better activity and less toxicity for the treatment of Hypertension is a goal of the major importance. In this sense, theoretical models as QSAR can be useful to discover new drugs for hypertension treatment. For this reason, we developed a new multi-target-QSAR (mt-QSAR) model to discover new drugs. A public databases ChEMBL contain Big Data sets of multitarget assays of inhibitors of a group of receptors with special relevance in Hypertension was used. However, almost all the computational models known focus in only one target or receptor. In this work, Beta-2 adrenergic receptor, Adrenergic receptor beta, Type-1 angiotensin II receptor, Angiotensin-converting enzyme, Betaadrenergic receptor, Cytochrome P450 11B2 and Renin were used as receptor inputs in the model. An ANN is our statistical analysis. In that way, we used as input Topological Indices, in specific Wiener, Barabasi and Harary indices calculated by Dragon software. These operators quantify the deviations of the structure of one drug from the expected values for all drugs assayed in different boundary conditions such as type of receptor, type of assay, type of target, target mapping. Overall training performance was 90%. Overall Validation predictability performance was 90%.

There are many parasite species with very different antiparasite drugs susceptibility. Computational methods in biology and chemistry prediction of the biological activity based on Quantitative Structure-Activity Relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multispecies QSAR classification model (ms-QSAR). In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a ms-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using entropy type indices. The data was processed by Artificial Neural Network (ANN) classifying drugs as active or nonactive against the different tested parasite species. The best ANN found was MLP 23:23-18-1:1. Overall model classification accuracy was 85.65% (211/244 cases) in training. Validation of the model was carried out by means of external predicting series. In this serie, the model classified correctly 81.85% (275/357 cases).